CA1323700C - Neural network based automated cytological specimen classification system and method - Google Patents
Neural network based automated cytological specimen classification system and methodInfo
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- CA1323700C CA1323700C CA000595659A CA595659A CA1323700C CA 1323700 C CA1323700 C CA 1323700C CA 000595659 A CA000595659 A CA 000595659A CA 595659 A CA595659 A CA 595659A CA 1323700 C CA1323700 C CA 1323700C
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Classifications
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- G—PHYSICS
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
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- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
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- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
- G01N15/10—Investigating individual particles
- G01N15/14—Electro-optical investigation, e.g. flow cytometers
- G01N2015/1488—Methods for deciding
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y10S706/00—Data processing: artificial intelligence
- Y10S706/902—Application using ai with detail of the ai system
- Y10S706/924—Medical
Abstract
ABSTRACT OF THE DISCLOSURE
An automated screening system and method for cytological specimen classification in which a neural network is utilized in performance of the classification function. Also included is an automated microscope and associated image processing circuitry.
An automated screening system and method for cytological specimen classification in which a neural network is utilized in performance of the classification function. Also included is an automated microscope and associated image processing circuitry.
Description
13237~0 Title: NEURAL NETWO~X ~ASED AUTOMATED~ICY~l~LC)G~ L:S~ECl~EN
CLASSIFICATION SYSTEM AND METHOD
TECHNICAL FIELD
Th~s invention relates generally, as indicated, to cell classification and, more partlcularly, to the use of neural networks and/or n~urocompu~erM for increa~ing the speed and accuracy of cell classi~ication.
The cervical smear tPap test) is the only mass screening cytological examination which reguires visual inspection of virtually every cell on the slide. The test suffers from a high false neg~tive rate due to the tedium and fatigue associated with its current manual mode of performance. Cell classification is typically performed on a "piece-work" basis by "cytotechnicians" employed by pathology laboratories and in some circumstances by salariad technicians. Due to the clearly life threat~ning nature of the false negative problem with its resultant possibility of undiagnosed cervical cancer, the American Cancer Society is considering doubling the frequency of recommended Pap smears. This, however, will certainly overload an already overburdened cervical screening industry as increasingly fewer individuals are willing to enter the tedious and stressful field oP manual cervical smear classification. An American Cancer Society reco~mendation to increase Pap smear frequency may only serve to increase the false negative rate by decreasing the amount of time spent on manual examination of each slide. ~ thorough manual examination should take no less than fifteen minutes per slide although a cytotechnician, especially one under a heavy workload, may spend less than half this amount of time. The College of American Pathology is well aware of this problem and would rapidly embrace an automated solution to cervical smear screening.
Due to the clear commercial potential ~or automated cervical smear analysis several attempts to this end have been made in the prior art. These attempts have proven to be 1 3 ~ 3 7 ~ O
unsuccessful since they have relied exclusively on classical pattern recognition technology (geometric, syntactic, template, statistical) or artif~cial intelligence (AI) based pattern recognition, l.e., rule-based expert system~. There i8, however, no claar algorlthm or complete and expllcit 6Qt of rule~ by which the human cytotechnlclan or pathol~gist uses his experience to comb~ne a multitude of fe~tures to make a classification in ge~talt manner. Cervical s~ear classification is, therefore, an excellent application for neural network based pattern recognition.
An example of the limitations of the prior art can be found in the 1987 reference entitled "Automated CerYical Screen Classification" by Tien et al, identified further below.
Background references of interest are, as follows:
Rumelhart, David E. and McClelland, James L., "Parallel Distributed Processing, n MIT Press, 1986, Volume l;
Tien, D. et al, "Automated Cervical Smear Classification," Proceedings of the IEE~/Ninth Annual Conference of the Engineering in Medicine and Biology Society, 1987, p. 1457-1458;
Hecht-Nielsen, Robert, "Neurocomputing: Picking the Human Brain," IEEE Spectrum, March, 1988, p 36-41; and Lippmann, Richard P., "An Introduction to Computing with Neural Nets,~ IE~E ASSP Magazine, April, 1987, p. 4-22.
BRIEF SUMMARY _ THE INVENTION
It is, therefore, a principal ob~ect of the present invention to provide an automated system and method for the classification of cytological specimens into categories, for example, categories of diagnostic significance.
Briefly, the invention lncludes an initial classifier (sometimes referred to as a primary classifier) preliminarily to classify a cytological specimen and a subsequent classifier (someti~es referred to as a secondary classifier) to classify those portions of the cytological specimen selected by the ~nit~al classifier for subsequent classification, wherein the ~3~37~0 subsequent classlfier includes a neural computer or neural network.
In one embodiment the pri~ary classifler may include a commercially ava~lable automated mlcroscope ln the ~orm Oe a s~andard cytology ~icroscope wlth a ~i~eo ca~era or CCD array with ths micro~cope stage controlled ~or automa~ic scanning of a slide. Image~ from the camer~ are digltized and outputted to the secondary classi~ier in the for~ o~ a computer sy~tem.
The computer system includes a neural networ~ as defined below and is disclosed, too, in several of the references referred to herein, which is utilized in the performance of cell image identification and classification into group~ of diagnostic interest. In an alternate embodiment the primary classifier may include a neural network. Other alternate embodiments also are disclosed below.
- It is a further ob;ect of the present invention that it perform its classification of a group of specimens within the period of time typically consumed for this task by careful manual screening (i.e., approximately 15 minutes/specimen).
It is a further ob;ect of the present invention that it perform its classification on cytological specimens which contain the numbers and types of objects other than single layers of cells of interest that are typically found in cervical smears (e.g., clumps of cells, overlapping cells, debris, leucocyte~, bacteria, mucus).
It is a further object of the present invention to perform the above-described classification on cervical smears for the detection of pre-malignant and malignant cells.
It is a further ob~ect of the present invention that it -perform its classification with smaller false negative error rates than typically found in conventional manual cervical smear screening.
An advantage of the cytological classification system of the present invention is that classification of cytological specimens into medically significant diagnostic categories will be mora reliable, i.e., with lower falsa negative error rates.
4 ~3~37~0 A further advantage of the cytological classification system of the present invention is that it does not require a modification in the procedure by which cellular specimens are obtained from the patient.
-A further advantage of the cytological classification system of the present invention is that it will permit reliable classification within processing time constraints that permit economically viable operation.
These and other objects, advantages and features of the present invention will become evident to those of ordinary skill in the art after having read the following detailed description of the preferred embodiment.
In a broad aspect, therefore, the present invention relates to :an automated cytological specimen classifier, comprising: (a) an automated microscope; (b) a video camera-charge coupled device; (c) an image digitizer; (d) a primary statistical classifier for detection of objects in a cytological specimen which exceed a threshold integrated optical density; and (e) a secondary classifier based on a neural network for detection of pre-malignant and malignant cells among the objects identified by the primary classifier.
In another broad aspect, the present invention relates to a method of classifying cytological specimens, comprising primarily classifying a specimen using a first classifier to determine 4(a) 13~37i~0 locations of interest and secondarily classifying such locations of interest using a neural network.
In another broad aspect, the present invention relates to an automated cytological specimen classifier for classifying cells contained in a smear on a slide to identify cells that are likely to be malignant or pre-malignant, comprising: (a) microscope mean~
for obtaining a view of at least part o~ a cytological specimen including cells and other material located generally randomly on a slide in an arrangement which can include other than a single layer o~ cells: (b~ camera means for creating an image of such view; (c) image digitizing means for producing a digital representation o~
such image: (d) a primary classifier means for detecting obiects in a digital representation of a cytological specimen based on a d~tectable feature, said primary classifier means co~prising a classifier means for detecting cells that are likely to be malignant or pre-malignant as well as other oells and material that initially appear to have characteristics of a malignant cell or a pre-malignant cell based on integrated optical density; and (e) a secondary classifier for distinguishing pre malignant and malignant cells from other cells and material among the objects detected b~
the primary classifier, said secondary classiier means comprising a neural computer apparatus means for effecting such distinguishing as a function of training thereof.
In yet another broad aspect, the present invention relates to a method of classifying cytological specimens, comprising using a 4(b~ 13~37~0 primary classifier apparatus primarily classifying a specimen which is generally randomly arranged and can include other than in a single layer to determine locations of interest, and a secondarily classifying such locations of interest using a neural network computer apparatus.
In still another broad aspect, the present invention relates to an automated cytological specimen classifier, comprising: (a) - microscope means for obtaining a view of at least part of a cytological specimen including cells and other material located generally randomly in an ar~angement which can include other than a single layer of cells; (b) camera means for creating an image of such view; (c) image digitizing means for producing a digital representation of such image; (d) primary classifier means for detecting objects in a digital representation of a cytological specimen based on a detectable feature, said primary classification means comprising a classifier for detecting cells that are likely to be of a predetermined cell type as well as other cells and material that initially appear to have characteristics of such predetermined cell type; and ~e) secondary classifier means for distinguishing cells of such predetermined cell type from other cells and material among the o~jects detected by said primary classifier means, said secondary classifier means comprising a neural computer apparatus means for effecting such distinguishing as a function of training thereof.
...
4(c) 13237~0 Moreover, it is noted here that the invention is described herein mainly with respect to classi~ication of cytologiaal specimens in the form of a cervical smear, e.g., as typically is done in connection with a Pap test. However, it will be appreciated that this is but one example of the application of the principles of the invention which are intended for application for classification of many other cytological specimens.
BRI~F DESCRIPTION OF THE DRAWINGS
: 10 In the annexed drawings:
Figure 1 is a block diagram for a neural network based automated cytological specimen screening device in accordance with the present invention;
Figure 2 is a representation o~ a three-layer neural network of the type utilized in the preferred embodiment;
Figure 3 is a block diagram of the alternate embodiment of the :~ automated screening device in accordance with the present invention:
Figure 4 is a block diagram of an alternate embodiment of the automated screening device in accordance with the present invention;
5 13~37~() Figure 5 iB a block diagram of an alternate embodiment of the automated screening device in accordance with the present invention;
Figure 6 iB a block diagram o~ an alternate embodlment of the automated screening device ln ~ccordance wlth th~ present invention: and ;~ Flgure 7 i8 a block diaqram of an alternate embodiment o~
J the automated screening device in accordance with the present - invention.
DESCRIPTION OF THE PREFERRED AND ALTERNATE EMBODIMENTS
Figure 1 illustrates a neural network based automated cytological specimen screening device in accordance with the present invention and referred to by the general reference numeral 10. The classification device 10 includes an ; automated microscope 11, a video camera or CCD device 12, an image digitizer 13, and classifier stages 14, 15, and 16.
The automated microscope ll effects relative movement of `~ the microscope objective and the specimen, and video camera or CCD 12 obtains an image or picture of a specific portion of the cytological specimen. The image is digit~zed by the image ; digitizer 13 and the information therefrom ~s coupled to the ~t`: classifier 14. In the preferred embodiment, classifier 14 is commercially available statistical classifier which ?~ ident~fies cell nuclel of interest by measurement of their integrated optical density (nuclear stain density). This is th~ ~um o~ the pixel grey values for the ob~ect, corrected for s optical errors. Compared to normal cell~, malignant cells - tend to possess a larger, more densely staining nucleu Ob~ects which pass classifier 14 consist of pre-malignant and malignant cells but also include other objects with high integrated optical density such as cell clumps, debris, leucocytes and mucus. The task of t~e secondary classifier 15 is to distinguish pre-malignant and malignant cells from these other objects.
A neural network is u~ilized to implement secondary classifier 15. Detailed descriptions of the design and opera~ion of neural networks suitable for implementation of 6 ~3~7~(~
secondary classifier 15 can be found ln the references cited herein. A brief description of this lnformation is provided below.
; Ba~ed on the data obtalned by the primary cla~sifler for the cytological specimen, the secondary classifier i8 used to check ~pecific areas o~ the specimen that are, for exampl~, determined to require further screening or clas~if~cation.
Such further examination by the secondary classi~ier may be effected by reliance on the already obtained digitized image data for the selected areas of the ~peci~en or by the taking of addit~onal data by the components 11-13 or by other commercially available op~ical or other equipment that would : provide acceptable data for use and anayl~is by the secondary classifier 15.
A neural network is a highly parallel distributed system with the topology of a directed graph. The nodes in neural networks are usually referred to as "processing elements" or "neurons" while the links are generally Xnown as "interconnects." Each processing element accepts multiple inputs and generates a single output signal which branches into multiple copies that are in turn distributed to the other processing elements as input signals. Information is stored in the strength of the connections known as weights. In an asynchronous fas~ion, each processing element co~putes the sum of products of the weight of each input line ~ultiplied by the signal level (usually 0 or 1) on that input line. If the su~
of products exceeds a preset activation threshold, the output of the processing element is set to 1, if les~, it is set to 0. Learning is achieved through adjustment of the values of the weights.
For the present invention, the preferred embodiment is achieved by utilization of a three-layer neural network of the type described in the Lippman reference as a "multi-layer perceptron" and discussed in detail in Chapter 8 of the Rumelhart reference. Other types of neural netowrk syste~s also may be used.
7 1'~37(~0 A three-layer neural network consi~t~ of an input layer, an output layer, and an intermediate hidden layer. The intermediate layer i~ required to allow for internal representatlon of patterns within the network. As shown by Min~ky and Papert in thelr 1969 book ontitled "Perceptrons"
(MIT Press), simple two-layer a880cl~tive networ~s are limited in the types o~ problems they can solve. A two-layer network with only "input" and noutput" processing elements ca~ only represent mappings in which similar input patterns lead to similar output patterns. Whenever the real word problem is not of this type, a three-layer networ~ i8 reguired. It has been shown that with a large enough hidden layer, a three-- layer neural networX can always find a representation that will map any input pattern to any desired output pattern. A
generic three-layer neural network of the type utilized in the preferred embodiment is shown in Figure 2.
Several important features of neural network architecture~ distinguish the~ from prior art approaches to the implementation of classifier 15.
1. There i8 little or no executive function. There are only v~ry simple units each performing its sum of products calculation. Each processing element's task is thus limited to receiving the inputs from its neighbors and, as a function of these inputs, computing an output ~alue which it ~ends to its neig~bor~. Each processing element per~orms this calculation periodically, in parallel with, but not synchronized to, the ~ctivities of any of its neiqhbors.
CLASSIFICATION SYSTEM AND METHOD
TECHNICAL FIELD
Th~s invention relates generally, as indicated, to cell classification and, more partlcularly, to the use of neural networks and/or n~urocompu~erM for increa~ing the speed and accuracy of cell classi~ication.
The cervical smear tPap test) is the only mass screening cytological examination which reguires visual inspection of virtually every cell on the slide. The test suffers from a high false neg~tive rate due to the tedium and fatigue associated with its current manual mode of performance. Cell classification is typically performed on a "piece-work" basis by "cytotechnicians" employed by pathology laboratories and in some circumstances by salariad technicians. Due to the clearly life threat~ning nature of the false negative problem with its resultant possibility of undiagnosed cervical cancer, the American Cancer Society is considering doubling the frequency of recommended Pap smears. This, however, will certainly overload an already overburdened cervical screening industry as increasingly fewer individuals are willing to enter the tedious and stressful field oP manual cervical smear classification. An American Cancer Society reco~mendation to increase Pap smear frequency may only serve to increase the false negative rate by decreasing the amount of time spent on manual examination of each slide. ~ thorough manual examination should take no less than fifteen minutes per slide although a cytotechnician, especially one under a heavy workload, may spend less than half this amount of time. The College of American Pathology is well aware of this problem and would rapidly embrace an automated solution to cervical smear screening.
Due to the clear commercial potential ~or automated cervical smear analysis several attempts to this end have been made in the prior art. These attempts have proven to be 1 3 ~ 3 7 ~ O
unsuccessful since they have relied exclusively on classical pattern recognition technology (geometric, syntactic, template, statistical) or artif~cial intelligence (AI) based pattern recognition, l.e., rule-based expert system~. There i8, however, no claar algorlthm or complete and expllcit 6Qt of rule~ by which the human cytotechnlclan or pathol~gist uses his experience to comb~ne a multitude of fe~tures to make a classification in ge~talt manner. Cervical s~ear classification is, therefore, an excellent application for neural network based pattern recognition.
An example of the limitations of the prior art can be found in the 1987 reference entitled "Automated CerYical Screen Classification" by Tien et al, identified further below.
Background references of interest are, as follows:
Rumelhart, David E. and McClelland, James L., "Parallel Distributed Processing, n MIT Press, 1986, Volume l;
Tien, D. et al, "Automated Cervical Smear Classification," Proceedings of the IEE~/Ninth Annual Conference of the Engineering in Medicine and Biology Society, 1987, p. 1457-1458;
Hecht-Nielsen, Robert, "Neurocomputing: Picking the Human Brain," IEEE Spectrum, March, 1988, p 36-41; and Lippmann, Richard P., "An Introduction to Computing with Neural Nets,~ IE~E ASSP Magazine, April, 1987, p. 4-22.
BRIEF SUMMARY _ THE INVENTION
It is, therefore, a principal ob~ect of the present invention to provide an automated system and method for the classification of cytological specimens into categories, for example, categories of diagnostic significance.
Briefly, the invention lncludes an initial classifier (sometimes referred to as a primary classifier) preliminarily to classify a cytological specimen and a subsequent classifier (someti~es referred to as a secondary classifier) to classify those portions of the cytological specimen selected by the ~nit~al classifier for subsequent classification, wherein the ~3~37~0 subsequent classlfier includes a neural computer or neural network.
In one embodiment the pri~ary classifler may include a commercially ava~lable automated mlcroscope ln the ~orm Oe a s~andard cytology ~icroscope wlth a ~i~eo ca~era or CCD array with ths micro~cope stage controlled ~or automa~ic scanning of a slide. Image~ from the camer~ are digltized and outputted to the secondary classi~ier in the for~ o~ a computer sy~tem.
The computer system includes a neural networ~ as defined below and is disclosed, too, in several of the references referred to herein, which is utilized in the performance of cell image identification and classification into group~ of diagnostic interest. In an alternate embodiment the primary classifier may include a neural network. Other alternate embodiments also are disclosed below.
- It is a further ob;ect of the present invention that it perform its classification of a group of specimens within the period of time typically consumed for this task by careful manual screening (i.e., approximately 15 minutes/specimen).
It is a further ob;ect of the present invention that it perform its classification on cytological specimens which contain the numbers and types of objects other than single layers of cells of interest that are typically found in cervical smears (e.g., clumps of cells, overlapping cells, debris, leucocyte~, bacteria, mucus).
It is a further object of the present invention to perform the above-described classification on cervical smears for the detection of pre-malignant and malignant cells.
It is a further ob~ect of the present invention that it -perform its classification with smaller false negative error rates than typically found in conventional manual cervical smear screening.
An advantage of the cytological classification system of the present invention is that classification of cytological specimens into medically significant diagnostic categories will be mora reliable, i.e., with lower falsa negative error rates.
4 ~3~37~0 A further advantage of the cytological classification system of the present invention is that it does not require a modification in the procedure by which cellular specimens are obtained from the patient.
-A further advantage of the cytological classification system of the present invention is that it will permit reliable classification within processing time constraints that permit economically viable operation.
These and other objects, advantages and features of the present invention will become evident to those of ordinary skill in the art after having read the following detailed description of the preferred embodiment.
In a broad aspect, therefore, the present invention relates to :an automated cytological specimen classifier, comprising: (a) an automated microscope; (b) a video camera-charge coupled device; (c) an image digitizer; (d) a primary statistical classifier for detection of objects in a cytological specimen which exceed a threshold integrated optical density; and (e) a secondary classifier based on a neural network for detection of pre-malignant and malignant cells among the objects identified by the primary classifier.
In another broad aspect, the present invention relates to a method of classifying cytological specimens, comprising primarily classifying a specimen using a first classifier to determine 4(a) 13~37i~0 locations of interest and secondarily classifying such locations of interest using a neural network.
In another broad aspect, the present invention relates to an automated cytological specimen classifier for classifying cells contained in a smear on a slide to identify cells that are likely to be malignant or pre-malignant, comprising: (a) microscope mean~
for obtaining a view of at least part o~ a cytological specimen including cells and other material located generally randomly on a slide in an arrangement which can include other than a single layer o~ cells: (b~ camera means for creating an image of such view; (c) image digitizing means for producing a digital representation o~
such image: (d) a primary classifier means for detecting obiects in a digital representation of a cytological specimen based on a d~tectable feature, said primary classifier means co~prising a classifier means for detecting cells that are likely to be malignant or pre-malignant as well as other oells and material that initially appear to have characteristics of a malignant cell or a pre-malignant cell based on integrated optical density; and (e) a secondary classifier for distinguishing pre malignant and malignant cells from other cells and material among the objects detected b~
the primary classifier, said secondary classiier means comprising a neural computer apparatus means for effecting such distinguishing as a function of training thereof.
In yet another broad aspect, the present invention relates to a method of classifying cytological specimens, comprising using a 4(b~ 13~37~0 primary classifier apparatus primarily classifying a specimen which is generally randomly arranged and can include other than in a single layer to determine locations of interest, and a secondarily classifying such locations of interest using a neural network computer apparatus.
In still another broad aspect, the present invention relates to an automated cytological specimen classifier, comprising: (a) - microscope means for obtaining a view of at least part of a cytological specimen including cells and other material located generally randomly in an ar~angement which can include other than a single layer of cells; (b) camera means for creating an image of such view; (c) image digitizing means for producing a digital representation of such image; (d) primary classifier means for detecting objects in a digital representation of a cytological specimen based on a detectable feature, said primary classification means comprising a classifier for detecting cells that are likely to be of a predetermined cell type as well as other cells and material that initially appear to have characteristics of such predetermined cell type; and ~e) secondary classifier means for distinguishing cells of such predetermined cell type from other cells and material among the o~jects detected by said primary classifier means, said secondary classifier means comprising a neural computer apparatus means for effecting such distinguishing as a function of training thereof.
...
4(c) 13237~0 Moreover, it is noted here that the invention is described herein mainly with respect to classi~ication of cytologiaal specimens in the form of a cervical smear, e.g., as typically is done in connection with a Pap test. However, it will be appreciated that this is but one example of the application of the principles of the invention which are intended for application for classification of many other cytological specimens.
BRI~F DESCRIPTION OF THE DRAWINGS
: 10 In the annexed drawings:
Figure 1 is a block diagram for a neural network based automated cytological specimen screening device in accordance with the present invention;
Figure 2 is a representation o~ a three-layer neural network of the type utilized in the preferred embodiment;
Figure 3 is a block diagram of the alternate embodiment of the :~ automated screening device in accordance with the present invention:
Figure 4 is a block diagram of an alternate embodiment of the automated screening device in accordance with the present invention;
5 13~37~() Figure 5 iB a block diagram of an alternate embodiment of the automated screening device in accordance with the present invention;
Figure 6 iB a block diagram o~ an alternate embodlment of the automated screening device ln ~ccordance wlth th~ present invention: and ;~ Flgure 7 i8 a block diaqram of an alternate embodiment o~
J the automated screening device in accordance with the present - invention.
DESCRIPTION OF THE PREFERRED AND ALTERNATE EMBODIMENTS
Figure 1 illustrates a neural network based automated cytological specimen screening device in accordance with the present invention and referred to by the general reference numeral 10. The classification device 10 includes an ; automated microscope 11, a video camera or CCD device 12, an image digitizer 13, and classifier stages 14, 15, and 16.
The automated microscope ll effects relative movement of `~ the microscope objective and the specimen, and video camera or CCD 12 obtains an image or picture of a specific portion of the cytological specimen. The image is digit~zed by the image ; digitizer 13 and the information therefrom ~s coupled to the ~t`: classifier 14. In the preferred embodiment, classifier 14 is commercially available statistical classifier which ?~ ident~fies cell nuclel of interest by measurement of their integrated optical density (nuclear stain density). This is th~ ~um o~ the pixel grey values for the ob~ect, corrected for s optical errors. Compared to normal cell~, malignant cells - tend to possess a larger, more densely staining nucleu Ob~ects which pass classifier 14 consist of pre-malignant and malignant cells but also include other objects with high integrated optical density such as cell clumps, debris, leucocytes and mucus. The task of t~e secondary classifier 15 is to distinguish pre-malignant and malignant cells from these other objects.
A neural network is u~ilized to implement secondary classifier 15. Detailed descriptions of the design and opera~ion of neural networks suitable for implementation of 6 ~3~7~(~
secondary classifier 15 can be found ln the references cited herein. A brief description of this lnformation is provided below.
; Ba~ed on the data obtalned by the primary cla~sifler for the cytological specimen, the secondary classifier i8 used to check ~pecific areas o~ the specimen that are, for exampl~, determined to require further screening or clas~if~cation.
Such further examination by the secondary classi~ier may be effected by reliance on the already obtained digitized image data for the selected areas of the ~peci~en or by the taking of addit~onal data by the components 11-13 or by other commercially available op~ical or other equipment that would : provide acceptable data for use and anayl~is by the secondary classifier 15.
A neural network is a highly parallel distributed system with the topology of a directed graph. The nodes in neural networks are usually referred to as "processing elements" or "neurons" while the links are generally Xnown as "interconnects." Each processing element accepts multiple inputs and generates a single output signal which branches into multiple copies that are in turn distributed to the other processing elements as input signals. Information is stored in the strength of the connections known as weights. In an asynchronous fas~ion, each processing element co~putes the sum of products of the weight of each input line ~ultiplied by the signal level (usually 0 or 1) on that input line. If the su~
of products exceeds a preset activation threshold, the output of the processing element is set to 1, if les~, it is set to 0. Learning is achieved through adjustment of the values of the weights.
For the present invention, the preferred embodiment is achieved by utilization of a three-layer neural network of the type described in the Lippman reference as a "multi-layer perceptron" and discussed in detail in Chapter 8 of the Rumelhart reference. Other types of neural netowrk syste~s also may be used.
7 1'~37(~0 A three-layer neural network consi~t~ of an input layer, an output layer, and an intermediate hidden layer. The intermediate layer i~ required to allow for internal representatlon of patterns within the network. As shown by Min~ky and Papert in thelr 1969 book ontitled "Perceptrons"
(MIT Press), simple two-layer a880cl~tive networ~s are limited in the types o~ problems they can solve. A two-layer network with only "input" and noutput" processing elements ca~ only represent mappings in which similar input patterns lead to similar output patterns. Whenever the real word problem is not of this type, a three-layer networ~ i8 reguired. It has been shown that with a large enough hidden layer, a three-- layer neural networX can always find a representation that will map any input pattern to any desired output pattern. A
generic three-layer neural network of the type utilized in the preferred embodiment is shown in Figure 2.
Several important features of neural network architecture~ distinguish the~ from prior art approaches to the implementation of classifier 15.
1. There i8 little or no executive function. There are only v~ry simple units each performing its sum of products calculation. Each processing element's task is thus limited to receiving the inputs from its neighbors and, as a function of these inputs, computing an output ~alue which it ~ends to its neig~bor~. Each processing element per~orms this calculation periodically, in parallel with, but not synchronized to, the ~ctivities of any of its neiqhbors.
2. All knowledge i~ in the connections. Only very short term storaqe can occur in the states of the processing elements. All lonq term storage i8 represented by the values of the connection strengths or "weights~ between the r ` processing elements. It i8 the rules that establish these weights and modify them for learning that primarily distinguish one neural network model from another. All ~nowledge is thus ~mplicitly represented in the strengths of the connection weights rather than explicitly represented in the states of the processing elements.
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8 ~3~37~
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8 ~3~37~
3. In contrast to algorithmie computers and expert systems, the goal of neural net learning i6 not the formulation o~ an algorlthm or a set of explicit rules.
During learning, a neural network self-organizes to establish the global set of welghts which will result ln itA output ~or a ~iven input mo t closely correspondinq to what lt i8 told i3 the correct output for that input. It i8 thls adaptive acquisition of connection strengths that allow3 a neural network to behave as if it knew the rules. Conventional computers excell in application~ where the knowledge can be ; readily represented in an explicit algorithm or an explicit and complete set of rules. Where this is not the case, conventional computers encounter great difficulty. While conventional computers can execute an algorithm much more rapidly than any human, they are challenged to match human performance in non-algorithmic tasks such as pattern recognition, nearest neighbor classification~ and arriving at the optimum solution when faced with multiple simultaneous constraints. If N exemplar patterns are to be searched in order to classify an unknown input pattern, an algorithmic system can accomplish thi~ tas~ in approximately order N time.
In a neural network, all of the candidate signatures are simultaneously represented by the global set of connection weights of the entire syctem. A neural network thus automatically arrives at the nearest neighbor to th~ ambiguous input in order 1 time as opposed to ~rder N time.
For the present invention, the preferre~ embodiment is achieved by utilization of a three-layer ~ackpropagation network as described in the Rumelhart reference for the neural network of classifier stage 15. Backpropagation ls described in detail in the Rumel~art reference. Briefly described, it operates as follows. During net training, errors (i.e., the difference between the appropriate output for an exemplar input and the current net outpu~ for that output) are propagated backwards from the output layer to the ~iddle layer and then to the input layer. These errors are util~zed at 13`~3700 g each layer by the training algorithm to read~ust the interconnection weights 80 that a future presentatlon of the - exemplar pattern will result in the appropriate output category. Following the net training, during the feed-~orward mode, unknown input pattern~ ~re classlfied by the neural network into the exemplar category which mo~t clo8ely re6embles it.
`~ The output of neural net classi~ier 15 indicate~ the g presence or absence of pre-malignant or malignant cells. Thelocation of the cells on the input slide i~ obtained from X-Y
plane position coordinates outputted continually by the automated microscope. This positlonal information ~s outputted to printer or video display 17 along with diagnosis and patient identification information so that the classification can be reviewed by a patholoqist.
In the preferred embodiment, the parallel structure o the neural network is emulated by execution with pipelined serial processing as performed by one of the commercially available neurocomputer accelerator boards. The operation of these neurocomputers is discussed in the Spectrum reference cited. The neural network preferably is a "Delta" processor, which is a commercially availa~le neurocomputer of Science Application International Corp. (SAIC) (see the Hecht-Nielsen reference above) that has demonstrated a susta~ned processing rate of lO interconnects/second in the feed-~orward (i.e., non-training) mode. For a typical cervlcal smear containing lO0,000 cells, 1-2S of ~he cells or approximately 1,500 images w111 require processing by classifier 15. As an example of the dat~ rates which result, assume that following data 3~ compression an image 50 x 50 pixels is processed by classifier 15. The input layer for the neural network, therefore, consists of 2,500 processing elements or "neurons. n The middle layer consists of approximately 25% of the input layer, or 625 neurons. (The number of output neurons is equal to the number of diagnostic categories of interest. This small nu~ber does not siqnificantly aff6ct this calculat~on.) The number of interconnects i~ thus ~2500)~625) or approximately 13~7~0 ; 6 1.5 x 10 . At a processing rate of 10 interco~nects/second, the processing by classifier 15 of the 1,500 images sent to it by classifier 14 will take le~s than four minute~. Currently available embodiments o~ classi~ier 14 operate at a rate of ; 50,000 cQlls/~inute (refer to the Tlen et al citation). With - classifier 14 operatlng at a rate of 50,000 cells/minute, the four minutes consumed by classifler 15 iB added to the two minutes used by clas~ifier 14 for a total of six minutes to analyze the 100,000 cell images on the slide. As discussed above, an accurate manual cervical ~mear analysis takes approximately lS minutes/slide. Prior art automated attempts using a non-neural network embod~ment of classifier 15 require over one hour/slide. This example is not meant in any way to limit the actual configuration of the present invention, but rather to demonstrate that it is capable of achievinq the object of processing cervical smears and other cytological samples within the time period required for commercially feasible operation.
In the preferred embodiment, primary classifier 14 i3 restricted to evaluation of the cellular nucleus while the ` secondary classifier 15 evaluates both the necleus and its - surrounding cytoplas~. The ratio between the nucleus and ^~ cytoplasm is an important indicator for pre-malignant and malignant cell classification. In a~ alternate embodiment, both classifier 14 and classifier 15 are limited to evaluation for the cellular nuclei.
Output information from the secondary classifier 15 is directed to an output monitor and printer 17, which may indicate a variety of information including, importantly, 30 whether any cells appear to be malignant or pre-malignant, appear to require further exa~ination, etc.
Figure 3 illustrates an alternate embodiment in which an additional neural net classifier stage 16 is added to pre-process the slide for large areas of artifactual material, i.e., ~aterial other than single layer cells of interest.
Thi~ includes clumps of cèlls, debris, mucus, leucocytes, etc.
11 ~30~70a Positional information obtained ~n this pre-screen i3 stored for use by th¢ remainder o~ the cla~sification ~ystem. The information from cla~si~ier stage 16 i8 utllized to llmit the proce~slng required by clas~i~ler 15. Classi~ier ~tage 14 can ignore ~ll materi~l within the areas de~ined by the po~itional coordinates outputted by clas~ifier 16. Thi8 wlll result ln less information being sent for process~ng by classifi~r 15.
A diagnosi~ iB, therefore, made on the basis of classif~cation of only those cells which lie outside of these areas. If an insufficient sample of cells lies outside of these areas for a valid diagnosis, this information will be outputted on 17 as an ~insufficient cell sample. n Figure 4 illustrates an alternate embodiment in which the imayes within the areas identified by classifier 16 are not ignored but are instead processed by a separate classif~er 18 which operates in parallel with classifier 15. ~he training of the neural net which composes classifier 18 i~ dedicated to the distinction of pre-malignant and malignant cells from said artifactual material.
Figure 5 illustrates an alternate embodiment wherein an additional non-neural net classification of nuclear morphological components, exclusive of integrated optical density, is placed between classifier 14 and classifier 15.
T~is classification is performed by classifier 19.
Figure 6 illustrates an alternate embodiment in which a com~ercially available SAIC neurocomputer is optimized for feed-forward processing 20. Through deletion of learning-mode capacity, all neurocomputer functions are dedicated to feed-forward operation. Learning is completed on a separate unmodified neurocomputer which contains both the learning and feed-forward functions.
Following the completion o~ learning, the final interconnection weights are transferred to the optimized feed-forward neurocomputer 20. Dedication of neurocomputer 20 to the feed-forward ~ode results ln a sustained feed-forward operation rate of 10 interconnects/second vs. lO
interconnects/second for the non-optimized board as 12 13~37~0 commercially supplled. The optlmlzed feed-forward neural network 20 is utillzed to perform the functlons of classifiers 14 and 16 in Figures 1, 3, 4, and 5. By utllizlng neural n~t classi~ier 20 to per~orm the functlon of ~tatlst~cal classlfier 14, cells of intere~t whlch are not necess~rily - mallgnant cervlcal cells, and wh~ch do not tbereforQ exceed the lntegrated optical denslty threshold of classlfier 14, would nevertheless be detected. An example would be the detection of endometrial cell~ which, while not necessarily indicative of cervical mallgnancy, are indicative of uterine malignancy when found in the Pap smear of a post-menopausal patient.
As an example of the data rates that result from this embodiment in Fig. 6, assume outside slide dim6ensions of 152m x 45mm or a total slide area of 675 x 10 micrometers .
'~ Neural net 20 processes a sliding window over this area for analysis. This window has dimensions of 20 micrometers x 20 micrometers or an area of 400 micrometers . There are, therefore, 1.5 x 10 of these windows on the 15mm x 45mm slide. For the primary classification function performed by neural net 20, a resolution of 1 micrometer/pixel is ; sufficient to detect those ob~ects which must be sent to ". , t secondary neural network classifier 15 for further analysis.
The input pattern for the image window analyzed by classifier ~ 20 is therefore 20 x 20 pixels or 400 neurons to the input - layer of neural net 20. T~e middle layer consists of approximately 25% of the input layer or 100 neurons. ~As discussed above in the data rate calculation for classifier - 15, the number of output layer neurons is small and does not significantly affect our results.) The number of interconnections in classifier 20 is thus approximatel~
; (400~(100) or 40 x 10 . At a processing rate of 10 -~ interconnects~second, each image from the sliding window will take 400 microseconds for neural net 20 to classify. In a 15mm x 45mm slide, there are 1.5 x 10 of the 400 micrometer windows which require classification by neural net 20. ~otal 13 13~37~0 clasæification time for neural net 20 ~8 therefore (1.5 x 10 )(400 x 10 ) = 600 seconds or ten minutes. If this ten minutes i8 added to the appriximately four mlnutes requir~d for secondary neural net clas~ifier 15, ~ total of 14 minutes/~lide result~. Thls example 1~ not meant in any way to limit the actual configuration Or the present lnventlon, but rather to demonstrate that it is capablo of achieving th3 ob~ect of proce6sing cervical smears and other cytological samples within the time period required for comemrc~ally feasible operation.
Speed of processinq data can be enhanced, too, by using parallel processing. For example, plural commercially available neurocomputers from SAIC can be coupled to effect ~-parallel processing of data, thus increasing overall operat~onal speed of the classifier usinq the same.
~Figure ~ illustrates an alternate embodiment in which ineural net primary classifier 20 iR utilized in con~unction with, rather than as a substitute for morphological classification and area classification. By dedication of classifier 20 to the detection of those few cell types which are of interest, but which cannot be detected by other means, `~the resolution required of classifier 20 is min$mized.
Althou ~ the present invention has been described in terms of the presently preferred embodiment, it is to be understood that such disclosure i9 not to ~e interpreted as limiting. Various alterations and modifications will no doubt become apparent to those skilled in the art after having read the above disclosure. Accordingly, it is intended that the appended claims be interpreted as covering all alterations and modifications as fall within t~e true spirit and scope of the invention.
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During learning, a neural network self-organizes to establish the global set of welghts which will result ln itA output ~or a ~iven input mo t closely correspondinq to what lt i8 told i3 the correct output for that input. It i8 thls adaptive acquisition of connection strengths that allow3 a neural network to behave as if it knew the rules. Conventional computers excell in application~ where the knowledge can be ; readily represented in an explicit algorithm or an explicit and complete set of rules. Where this is not the case, conventional computers encounter great difficulty. While conventional computers can execute an algorithm much more rapidly than any human, they are challenged to match human performance in non-algorithmic tasks such as pattern recognition, nearest neighbor classification~ and arriving at the optimum solution when faced with multiple simultaneous constraints. If N exemplar patterns are to be searched in order to classify an unknown input pattern, an algorithmic system can accomplish thi~ tas~ in approximately order N time.
In a neural network, all of the candidate signatures are simultaneously represented by the global set of connection weights of the entire syctem. A neural network thus automatically arrives at the nearest neighbor to th~ ambiguous input in order 1 time as opposed to ~rder N time.
For the present invention, the preferre~ embodiment is achieved by utilization of a three-layer ~ackpropagation network as described in the Rumelhart reference for the neural network of classifier stage 15. Backpropagation ls described in detail in the Rumel~art reference. Briefly described, it operates as follows. During net training, errors (i.e., the difference between the appropriate output for an exemplar input and the current net outpu~ for that output) are propagated backwards from the output layer to the ~iddle layer and then to the input layer. These errors are util~zed at 13`~3700 g each layer by the training algorithm to read~ust the interconnection weights 80 that a future presentatlon of the - exemplar pattern will result in the appropriate output category. Following the net training, during the feed-~orward mode, unknown input pattern~ ~re classlfied by the neural network into the exemplar category which mo~t clo8ely re6embles it.
`~ The output of neural net classi~ier 15 indicate~ the g presence or absence of pre-malignant or malignant cells. Thelocation of the cells on the input slide i~ obtained from X-Y
plane position coordinates outputted continually by the automated microscope. This positlonal information ~s outputted to printer or video display 17 along with diagnosis and patient identification information so that the classification can be reviewed by a patholoqist.
In the preferred embodiment, the parallel structure o the neural network is emulated by execution with pipelined serial processing as performed by one of the commercially available neurocomputer accelerator boards. The operation of these neurocomputers is discussed in the Spectrum reference cited. The neural network preferably is a "Delta" processor, which is a commercially availa~le neurocomputer of Science Application International Corp. (SAIC) (see the Hecht-Nielsen reference above) that has demonstrated a susta~ned processing rate of lO interconnects/second in the feed-~orward (i.e., non-training) mode. For a typical cervlcal smear containing lO0,000 cells, 1-2S of ~he cells or approximately 1,500 images w111 require processing by classifier 15. As an example of the dat~ rates which result, assume that following data 3~ compression an image 50 x 50 pixels is processed by classifier 15. The input layer for the neural network, therefore, consists of 2,500 processing elements or "neurons. n The middle layer consists of approximately 25% of the input layer, or 625 neurons. (The number of output neurons is equal to the number of diagnostic categories of interest. This small nu~ber does not siqnificantly aff6ct this calculat~on.) The number of interconnects i~ thus ~2500)~625) or approximately 13~7~0 ; 6 1.5 x 10 . At a processing rate of 10 interco~nects/second, the processing by classifier 15 of the 1,500 images sent to it by classifier 14 will take le~s than four minute~. Currently available embodiments o~ classi~ier 14 operate at a rate of ; 50,000 cQlls/~inute (refer to the Tlen et al citation). With - classifier 14 operatlng at a rate of 50,000 cells/minute, the four minutes consumed by classifler 15 iB added to the two minutes used by clas~ifier 14 for a total of six minutes to analyze the 100,000 cell images on the slide. As discussed above, an accurate manual cervical ~mear analysis takes approximately lS minutes/slide. Prior art automated attempts using a non-neural network embod~ment of classifier 15 require over one hour/slide. This example is not meant in any way to limit the actual configuration of the present invention, but rather to demonstrate that it is capable of achievinq the object of processing cervical smears and other cytological samples within the time period required for commercially feasible operation.
In the preferred embodiment, primary classifier 14 i3 restricted to evaluation of the cellular nucleus while the ` secondary classifier 15 evaluates both the necleus and its - surrounding cytoplas~. The ratio between the nucleus and ^~ cytoplasm is an important indicator for pre-malignant and malignant cell classification. In a~ alternate embodiment, both classifier 14 and classifier 15 are limited to evaluation for the cellular nuclei.
Output information from the secondary classifier 15 is directed to an output monitor and printer 17, which may indicate a variety of information including, importantly, 30 whether any cells appear to be malignant or pre-malignant, appear to require further exa~ination, etc.
Figure 3 illustrates an alternate embodiment in which an additional neural net classifier stage 16 is added to pre-process the slide for large areas of artifactual material, i.e., ~aterial other than single layer cells of interest.
Thi~ includes clumps of cèlls, debris, mucus, leucocytes, etc.
11 ~30~70a Positional information obtained ~n this pre-screen i3 stored for use by th¢ remainder o~ the cla~sification ~ystem. The information from cla~si~ier stage 16 i8 utllized to llmit the proce~slng required by clas~i~ler 15. Classi~ier ~tage 14 can ignore ~ll materi~l within the areas de~ined by the po~itional coordinates outputted by clas~ifier 16. Thi8 wlll result ln less information being sent for process~ng by classifi~r 15.
A diagnosi~ iB, therefore, made on the basis of classif~cation of only those cells which lie outside of these areas. If an insufficient sample of cells lies outside of these areas for a valid diagnosis, this information will be outputted on 17 as an ~insufficient cell sample. n Figure 4 illustrates an alternate embodiment in which the imayes within the areas identified by classifier 16 are not ignored but are instead processed by a separate classif~er 18 which operates in parallel with classifier 15. ~he training of the neural net which composes classifier 18 i~ dedicated to the distinction of pre-malignant and malignant cells from said artifactual material.
Figure 5 illustrates an alternate embodiment wherein an additional non-neural net classification of nuclear morphological components, exclusive of integrated optical density, is placed between classifier 14 and classifier 15.
T~is classification is performed by classifier 19.
Figure 6 illustrates an alternate embodiment in which a com~ercially available SAIC neurocomputer is optimized for feed-forward processing 20. Through deletion of learning-mode capacity, all neurocomputer functions are dedicated to feed-forward operation. Learning is completed on a separate unmodified neurocomputer which contains both the learning and feed-forward functions.
Following the completion o~ learning, the final interconnection weights are transferred to the optimized feed-forward neurocomputer 20. Dedication of neurocomputer 20 to the feed-forward ~ode results ln a sustained feed-forward operation rate of 10 interconnects/second vs. lO
interconnects/second for the non-optimized board as 12 13~37~0 commercially supplled. The optlmlzed feed-forward neural network 20 is utillzed to perform the functlons of classifiers 14 and 16 in Figures 1, 3, 4, and 5. By utllizlng neural n~t classi~ier 20 to per~orm the functlon of ~tatlst~cal classlfier 14, cells of intere~t whlch are not necess~rily - mallgnant cervlcal cells, and wh~ch do not tbereforQ exceed the lntegrated optical denslty threshold of classlfier 14, would nevertheless be detected. An example would be the detection of endometrial cell~ which, while not necessarily indicative of cervical mallgnancy, are indicative of uterine malignancy when found in the Pap smear of a post-menopausal patient.
As an example of the data rates that result from this embodiment in Fig. 6, assume outside slide dim6ensions of 152m x 45mm or a total slide area of 675 x 10 micrometers .
'~ Neural net 20 processes a sliding window over this area for analysis. This window has dimensions of 20 micrometers x 20 micrometers or an area of 400 micrometers . There are, therefore, 1.5 x 10 of these windows on the 15mm x 45mm slide. For the primary classification function performed by neural net 20, a resolution of 1 micrometer/pixel is ; sufficient to detect those ob~ects which must be sent to ". , t secondary neural network classifier 15 for further analysis.
The input pattern for the image window analyzed by classifier ~ 20 is therefore 20 x 20 pixels or 400 neurons to the input - layer of neural net 20. T~e middle layer consists of approximately 25% of the input layer or 100 neurons. ~As discussed above in the data rate calculation for classifier - 15, the number of output layer neurons is small and does not significantly affect our results.) The number of interconnections in classifier 20 is thus approximatel~
; (400~(100) or 40 x 10 . At a processing rate of 10 -~ interconnects~second, each image from the sliding window will take 400 microseconds for neural net 20 to classify. In a 15mm x 45mm slide, there are 1.5 x 10 of the 400 micrometer windows which require classification by neural net 20. ~otal 13 13~37~0 clasæification time for neural net 20 ~8 therefore (1.5 x 10 )(400 x 10 ) = 600 seconds or ten minutes. If this ten minutes i8 added to the appriximately four mlnutes requir~d for secondary neural net clas~ifier 15, ~ total of 14 minutes/~lide result~. Thls example 1~ not meant in any way to limit the actual configuration Or the present lnventlon, but rather to demonstrate that it is capablo of achieving th3 ob~ect of proce6sing cervical smears and other cytological samples within the time period required for comemrc~ally feasible operation.
Speed of processinq data can be enhanced, too, by using parallel processing. For example, plural commercially available neurocomputers from SAIC can be coupled to effect ~-parallel processing of data, thus increasing overall operat~onal speed of the classifier usinq the same.
~Figure ~ illustrates an alternate embodiment in which ineural net primary classifier 20 iR utilized in con~unction with, rather than as a substitute for morphological classification and area classification. By dedication of classifier 20 to the detection of those few cell types which are of interest, but which cannot be detected by other means, `~the resolution required of classifier 20 is min$mized.
Althou ~ the present invention has been described in terms of the presently preferred embodiment, it is to be understood that such disclosure i9 not to ~e interpreted as limiting. Various alterations and modifications will no doubt become apparent to those skilled in the art after having read the above disclosure. Accordingly, it is intended that the appended claims be interpreted as covering all alterations and modifications as fall within t~e true spirit and scope of the invention.
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Claims (54)
1. An automated cytological specimen classifier, comprising:
(a) an automated microscope;
(b) a video camera-charge coupled device;
(c) an image digitizer;
(d) a primary statistical classifier for detection of objects in a cytological specimen which exceed a threshold integrated optical density; and (e) a secondary classifier based on a neural network for detection of pre-malignant and malignant cells among the objects identified by the primary classifier.
(a) an automated microscope;
(b) a video camera-charge coupled device;
(c) an image digitizer;
(d) a primary statistical classifier for detection of objects in a cytological specimen which exceed a threshold integrated optical density; and (e) a secondary classifier based on a neural network for detection of pre-malignant and malignant cells among the objects identified by the primary classifier.
2. The automated cytological classifier of Claim 1 further comprising a neural network pre-screening classifier for recognition and classification of general areas within a specimen that contain material other than a cellular monolayer.
3. The automated classifier of Claim 2 wherein the output from the pre-screening classifier is utilized to exclude the said identified areas from analysis by the secondary classifier.
4. The automated classifier of Claim 2 wherein the output from the pre-screening classifier is utilized to modify the secondary classification on images found within the areas of the specimen identified by said pre-screening classifier.
5. The automated classifier of Claim 1 wherein the primary statistical classifier is restricted to evaluation of the cellular nucleus while the secondary classifier evaluates both the cellular nucleus and its surrounding cytoplasm.
6. The automated classifier of Claim 1 wherein both the primary statistical classifier and the secondary classifier are restricted to evaluation of the cellular nucleus.
7. The automated classifier of Claim 1 further comprising means for making an additional non-neural net classification of nuclear morphological components in addition to integrated optical density, said means being coupled between the primary and secondary classifier.
8. An automated cytological specimen classifier, comprising a low resolution neural network for performing primary classification of a cytological specimen, a high resolution neural network for performing secondary classification, and means for coupling said low and high resolution neural networks to pass data representing locations of interest from the former to the latter.
9. A method of classifying cytological specimens, comprising primarily classifying a specimen using a first classifier to determine locations of interest and secondarily classifying such locations of interest using a neural network.
10. The method of Claim 9, wherein said primarily classifying comprises using a video camera or charge coupled device (CCD) to obtain images of the specimen, a digitizer to digitize such images and an integrated optical density detector.
11. The method of Claim 9, wherein said primary classifying comprises using a neural network.
12. An automated cytological specimen classifier for classifying cells contained in a smear on a slide to identify cells that are likely to be malignant or pre-malignant, comprising:
(a) microscope means for obtaining a view of at least part of a cytological specimen including cells and other material located generally randomly on a slide in an arrangement which can include other than a single layer of cells;
(b) camera means for creating an image of such view;
(c) image digitizing means for producing a digital representation of such image;
(d) a primary classifier means for detecting objects in a digital representation of a cytological specimen based on a detectable feature, said primary classifier means comprising a classifier means for detecting cells that are likely to be malignant or pre-malignant as well as other cells and material that initially appear to have characteristics of a malignant cell or a pre-malignant cell based on integrated optical density; and (e) a secondary classifier for distinguishing pre-malignant and malignant cells from other cells and material among the objects detected by the primary classifier, said secondary classifier means comprising a neural computer apparatus means for effecting such distinguishing as a function of training thereof.
(a) microscope means for obtaining a view of at least part of a cytological specimen including cells and other material located generally randomly on a slide in an arrangement which can include other than a single layer of cells;
(b) camera means for creating an image of such view;
(c) image digitizing means for producing a digital representation of such image;
(d) a primary classifier means for detecting objects in a digital representation of a cytological specimen based on a detectable feature, said primary classifier means comprising a classifier means for detecting cells that are likely to be malignant or pre-malignant as well as other cells and material that initially appear to have characteristics of a malignant cell or a pre-malignant cell based on integrated optical density; and (e) a secondary classifier for distinguishing pre-malignant and malignant cells from other cells and material among the objects detected by the primary classifier, said secondary classifier means comprising a neural computer apparatus means for effecting such distinguishing as a function of training thereof.
13. The automated classifier of Claim 12, wherein said primary classifier means comprises a statistical classifier.
14. The automated classifier of Claim 12, wherein such cytological specimen includes overlapping cells.
15. The automated classifier of Claim 12 wherein said secondary classifier means is operable to distinguish pre-malignant and malignant cells among overlapping arrangements of cells and other material.
16. The automated classifier of Claim 12, wherein said camera means is positioned to create an image of a portion of such cytological specimen from such view.
17. The automated classifier of Claim 12, said neural computer apparatus means comprising an electronic neural computer.
18. The automated classifier of Claim 12 wherein the primary classifier means is restricted to evaluation of the cellular nucleus while the secondary classifier means evaluates both the cellular nucleus and its surrounding cytoplasm.
19. The automated classifier of Claim 12 wherein both the primary classifier means and the secondary classifier means are restricted to evaluation of the cellular nucleus.
20. The automated classifier of Claim 12 further comprising means for making an additional non-neural net classification of nuclear morphological components in addition to integrated optical density, said means being coupled between the primary classifier means and the secondary classifier means.
21. The automated classifier of Claim 12, said microscope means comprising an automated microscope.
22. The automated classifier of Claim 12, said camera means comprising a video camera.
23. The automated classifier of Claim 12, said camera means comprising a charge coupled device.
24. The automated classifier of Claim 12, said primary classifier means comprising means for detection of objects in such digital representation of a cytological specimen which has a feature that exceeds a threshold level.
25. The automated classifier of Claim 12, said primary classifier means comprising means for detection of objects in such digital representation of a cytological specimen which has a feature that exceeds a threshold integrated optical density.
26. The automated classifier of Claim 12, said primary classifier means comprising means for detection of objects in such digital representation of a cytological specimen based on morphological criteria.
27. The automated classifier of Claim 12 further comprising a neural network pre-screening classifier means for recognition and classification of general areas within the digital representation of a specimen that contain material other than a cellular monolayer prior to primary classification by said primary classifier means.
28. The automated classifier of Claim 27, wherein the output from the pre-screening classifier means is utilized to exclude such areas from further analysis.
29. The automated classifier of Claim 27, wherein the output from the pre-screening classifier means is utilized to modify further analysis of images found within such areas.
30. A method of classifying cytological specimens, comprising using a primary classifier apparatus primarily classifying a specimen which is generally randomly arranged and can include other than a single layer to determine locations of interest, and secondarily classifying such locations of interest using a neural network computer apparatus.
31. The method of Claim 30, wherein said primary classifying step comprises using a video camera or charge coupled device (CCD) to obtain images of the specimen, a digitizer to digitize such images and an integrated optical density detector.
32. The method of Claim 30, wherein said primary classifying comprises using a neural network computer apparatus.
33. The method of Claim 30, wherein said step of using a primary classifier apparatus primarily classifying a specimen comprises using a statistical classifier.
34. The method of Claim 30, wherein said step of using a primary classifier apparatus primarily classifying a specimen comprises making a classification based on morphology.
35. The method of Claim 30, wherein said step of using a primary classifier apparatus primarily classifying a specimen comprises making such primary classification based on integrated optical density.
36. The method of Claim 30, further comprising training such neural network computer apparatus to identify cytological specimens of interest.
37. An automated cytological specimen classifier, comprising:
(a) microscope means for obtaining a view of at least part of a cytological specimen including cells and other material located generally randomly in an arrangement which can include other than a single layer of cells;
(b) camera means for creating an image of such view;
(c) image digitizing means for producing a digital representation of such image;
(d) primary classifier means for detecting objects in a digital representation of a cytological specimen based on a detectable feature, said primary classification means comprising a classifier for detecting cells that are likely to be of a predetermined cell type as well as other cells and material that initially appear to have characteristics of such predetermined cell type; and (e) secondary classifier means for distinguishing cells of such predetermined cell type from other cells and material among the objects detected by said primary classifier means, said secondary classifier means comprising a neural computer apparatus means for effecting such distinguishing as a function of training thereof.
(a) microscope means for obtaining a view of at least part of a cytological specimen including cells and other material located generally randomly in an arrangement which can include other than a single layer of cells;
(b) camera means for creating an image of such view;
(c) image digitizing means for producing a digital representation of such image;
(d) primary classifier means for detecting objects in a digital representation of a cytological specimen based on a detectable feature, said primary classification means comprising a classifier for detecting cells that are likely to be of a predetermined cell type as well as other cells and material that initially appear to have characteristics of such predetermined cell type; and (e) secondary classifier means for distinguishing cells of such predetermined cell type from other cells and material among the objects detected by said primary classifier means, said secondary classifier means comprising a neural computer apparatus means for effecting such distinguishing as a function of training thereof.
38. The automated classifier of Claim 37, wherein the primary classifier means is restricted to evaluation of the cellular nucleus while the secondary classifier means evaluates both the cellular nucleus and its surrounding cytoplasm.
39. The automated classifier of Claim 37, wherein both the primary classifier means and the secondary classifier means are restricted to evaluation of the cellular nucleus.
40. The automated classifier of Claim 37, further comprising means for making an additional non-neural net classification of nuclear morphological components, said means being coupled between the primary classifier means and said secondary classifier means.
41. The automated classifier of Claim 37, said microscope means comprising an automated microscope.
42. The automated classifier of Claim 37, said camera means comprising a video camera.
43. The automated classifier of Claim 37, said camera means comprising a charge coupled device.
44. The automated classifier of Claim 37, said primary classifier means comprising means for detection of objects in such digital representation of a cytological specimen which have a feature that exceeds a threshold level.
45. The automated classifier of Claim 37, said primary classifier means comprising means for detection of objects in such digital representation of a cytological specimen which has a feature that exceeds a threshold integrated optical density.
46. The automated classifier of Claim 37, said primary classifier means comprising means for detection of objects in such digital representation of a cytological specimen based on morphological criteria.
47. The automated classifier of Claim 37, wherein said camera means is positioned to create an image of a portion of such cytological specimen from such view.
48. The automated classifier of Claim 37, said neural computer apparatus means comprising an electronic neural computer.
49. The automated classifier of Claim 37, wherein primary classifier means comprises a statistical classifier.
50. The automated classifier of Claim 37, wherein such cytological specimen includes overlapping cells.
51. The automated classifier of Claim 37, wherein said secondary classifier means is operable to distinguish cells of such predetermined cell type among overlapping arrangements of cells and other materials.
52. The automated classifier of Claim 37 further comprising a neural network pre-screening classifier means for identifying general areas within the digital representation of a specimen that contain material other than a cellular monolayer prior to primary classification.
53. The automated classifier of Claim 52, wherein the output from the pre-screening classifier is utilized to exclude such identified areas from further analysis.
54. The automated classifier of Claim 52, wherein the output from the pre-screening classifier means is utilized to modify further analysis of images found within the areas of the specimen identified by said pre-screening classifier means.
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1988
- 1988-04-08 US US07179060 patent/US4965725B1/en not_active Expired - Lifetime
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1989
- 1989-03-22 DE DE68926796T patent/DE68926796T2/en not_active Expired - Fee Related
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- 1989-03-22 SG SG1996004815A patent/SG46454A1/en unknown
- 1989-03-22 ES ES89302889T patent/ES2090033T3/en not_active Expired - Lifetime
- 1989-03-22 AT AT89302889T patent/ATE140327T1/en not_active IP Right Cessation
- 1989-03-24 RO RO146063A patent/RO106931B1/en unknown
- 1989-03-24 JP JP1504988A patent/JPH04501325A/en active Pending
- 1989-03-24 MC MC892101D patent/MC2101A1/en unknown
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- 1989-03-24 RU SU894831388A patent/RU2096827C1/en not_active IP Right Cessation
- 1989-03-24 BR BR898907355A patent/BR8907355A/en not_active IP Right Cessation
- 1989-03-24 HU HU892848A patent/HU208186B/en not_active IP Right Cessation
- 1989-04-04 CA CA000595659A patent/CA1323700C/en not_active Expired - Fee Related
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- 1989-04-07 CN CN89102194A patent/CN1031811C/en not_active Expired - Fee Related
- 1989-10-11 US US07420105 patent/US5287272B1/en not_active Expired - Lifetime
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1990
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1996
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1998
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