Search Images Maps Play YouTube News Gmail Drive More »
Sign in
Screen reader users: click this link for accessible mode. Accessible mode has the same essential features but works better with your reader.

Patents

  1. Advanced Patent Search
Publication numberUS20050136509 A1
Publication typeApplication
Application numberUS 10/938,314
Publication dateJun 23, 2005
Filing dateSep 10, 2004
Priority dateSep 10, 2003
Also published asUS7941275, US7979212, US8515683, US20050266395, US20060014238, US20110311123, WO2005027015A2, WO2005027015A3
Publication number10938314, 938314, US 2005/0136509 A1, US 2005/136509 A1, US 20050136509 A1, US 20050136509A1, US 2005136509 A1, US 2005136509A1, US-A1-20050136509, US-A1-2005136509, US2005/0136509A1, US2005/136509A1, US20050136509 A1, US20050136509A1, US2005136509 A1, US2005136509A1
InventorsGauri Gholap, Abhijeet Gholap
Original AssigneeBioimagene, Inc.
Export CitationBiBTeX, EndNote, RefMan
External Links: USPTO, USPTO Assignment, Espacenet
Apparatus which utilizes digtal image analysis and immunohistochemical staining as tool in classification, diagnosis and prognosis of cancer
US 20050136509 A1
Abstract
A method and system analyzing tissue or cell samples stored on laboratory slides or other media to identify properties of medical predictive and diagnostic relevance. The method and system includes automatically analyzing plural digital images created from plural biological tissue samples to which a staining reagent or a an immunohistochemical (IHC) compound has been applied, automatically quantitatively analyzing the relevant properties of the digital images, and creating interpretive data, images and reports resulting from such analysis.
Images(9)
Previous page
Next page
Claims(20)
1. A pathological analysis system, comprising in combination:
a digital image analysis module for automatically analyzing a plurality of digital images created from a plurality of biological tissue samples to which an staining reagent has been applied to determine one or more areas of interest and for automatically analyzing a plurality of digital images created from a plurality of biological tissues samples to which an immunohistochemical compound has been applied to determine one or more areas of interest;
an medical analysis module for automatically quantitatively analyzing the one or more determined areas of interest to automatically generate additional interpretive images, medical data, medial statistics or medical reports of predictive value or diagnostic value;
a display module to display the plurality of digital images, the additional interpretive images, the medical data, medical statistics or medical reports on a graphical user interface display; and
a recorder module to automatically record, store and apply knowledge generated by the quantitatively analysis of the one or more determined areas of interest.
2. The pathological analysis of claim 1 in which the digital image analysis module automatically analyzes a plurality of digital images which an staining reagent has been applied to determine one or more areas of interest in which one or more human cancer cells may be present.
3. The pathological analysis system of claim 1 in which the digital image analysis module includes automatically analyzing a plurality of digital images to which a staining reagent has been applied to determine one or more areas of interest in which one or more human cancer cells may be present using automatic chromatin pattern analysis, automatic nucleoar pattern analysis or automatic mitotic activity pattern analysis.
4. The pathological analysis system of claim 1 in which the digital image analysis module includes automatically analyzing a plurality of digital images created from a plurality of biological tissues samples to which an immunohistochemical (IHC) compound has been applied to determine one or more areas of interest in which one or more human cancer cells may be present using automatic nuclear pattern analysis, automatic cytoplasmic pattern analysis or automatic membrane pattern analysis.
5. The pathological analysis system of claim 1 further comprising:
a microscope; and
a digital camera.
6. A pathological analysis system, comprising in combination:
a means for automatically analyzing a plurality of digital images created from a plurality of biological tissue samples to which an staining reagent has been applied to determine one or more areas of interest and for automatically analyzing a plurality of digital images created from a plurality of biological tissues samples to which an immunohistochemical compound has been applied to determine one or more areas of interest;
a means for automatically quantitatively analyzing the one or more determined areas of interest to automatically generate additional interpretive images, medical data, medial statistics or medical reports of predictive value or diagnostic value;
a means for displaying the plurality of digital images, the additional interpretive images, the medical data, medical statistics or medical reports on a graphical user interface display; and
a means for automatically recording, storing and applying knowledge generated by the quantitatively analysis of the one or more determined areas of interest.
7. The pathological analysis of claim 6 in which the a means for automatically analyzing a plurality of digital images automatically analyzes a plurality of digital images which an staining reagent has been applied to determine one or more areas of interest in which one or more human cancer cells may be present.
8. The pathological analysis system of claim 6 in which the means for automatically analyzing a plurality of digital images includes automatically analyzing a plurality of digital images to which a staining reagent has been applied to determine one or more areas of interest in which one or more human cancer cells may be present using automatic chromatin pattern analysis, automatic nucleoar pattern analysis or automatic mitotic activity pattern analysis.
9. The pathological analysis system of claim 6 in which the means for automatically analyzing a plurality of digital images includes automatically analyzing a plurality of digital images created from a plurality of biological tissues samples to which an immunohistochemical (IHC) compound has been applied to determine one or more areas of interest in which one or more human cancer cells may be present using automatic nuclear nuclear pattern analysis, automatic cytoplasmic pattern analysis or automatic membrane pattern analysis.
10. The pathological analysis system of claim 6 further comprising:
a means for magnifying microscopic biological tissue samples; and
a means for creating digital photographs.
11. A method for automatically creating a medical diagnosis, comprising:
acquiring a plurality of digital images created from the plurality of biological tissue samples to which a staining reagent has been applied;
automatically pre-processing the plurality of digital images to adjust, if necessary, a contrast level and a color level;
automatically performing a histogram analysis on the plurality of digital images using grey and red, green, blue (RGB) luminosity values to locate one or more areas of interest;
automatically determining one or more areas of interest in which one or more cancer cells may be present using automatic chromatin pattern analysis, automatic nucleoar pattern analysis or automatic mitotic activity pattern analysis; and
automatically classifying the determined one or more areas of interest in which one or more cancer cells may be present, thereby creating a medical diagnosis.
12. The method of claim 11 further comprising a computer readable medium having stored therein instructions for causing a processor to execute the steps of the method.
13. The method of claim 11 in which the staining reagent includes a Hematoxillin and Eosin (H&E) staining reagent.
14. The method of claim 11 further comprising:
creating new medical knowledge using the determined one or more areas of interest which one or more cancer cells may be present; and
automatically adjusting the processing completed on the plurality of digital images to further refine the ability to automatically detect and classify one or more cancer cells to create a medical diagnosis.
15. A method for automatically creating a medical diagnosis, comprising:
acquiring a plurality of digital images created from the plurality of biological tissue samples to which an immunohistochemical (IHC) compound has been applied;
automatically pre-processing the plurality of digital images to adjust, if necessary, a contrast level and a color level;
automatically performing a histogram analysis on the plurality of digital images using grey and red, green, blue (RGB) luminosity values to locate one or more areas of interest;
automatically determining one or more areas of interest in which one or more cancer cells may be present using automatic nuclear pattern analysis, automatic cytoplasmic pattern analysis or automatic membrane pattern analysis; and
automatically classifying the determined one or more areas of interest in which one or more cancer cells may be present, thereby creating a medical diagnosis.
16. The method of claim 15 further comprising a computer readable medium having stored therein instructions for causing a processor to execute the steps of the method.
17. The method of claim 15 further comprising:
creating new medical knowledge using the determined one or more areas of interest which one or more cancer cells may be present; and
automatically adjusting the processing completed on the plurality of digital images to further refine the ability to automatically detect and classify one or more cancer cells to create a medical diagnosis.
18. A method for completing a pathological diagnosis with a pathiam, comprising:
automatically analyzing a plurality of digital image intensity values from a plurality of digital images created from a plurality of biological tissue samples to which a staining reagent or a an immunohistochemical (IHC) compound has been applied to determine one or more areas of interest;
automatically classifying the determined one or more areas of interest into one or more patterns used to locate one or more morphological features from the biological tissue samples in which one or more human cancer cells may be present;
automatically creating a pathological diagnosis using the classified one or more patterns.
19. The method of claim 18 further comprising a computer readable medium having stored therein instructions for causing a processor to execute the steps of the method.
20. The method of claim 18 staining reagent includes a Hematoxillin and Eosin (H&E) staining reagent.
Description
CROSS REFERENCES TO RELATED APPLICATIONS

This patent application claims priority to U.S. Provisional Patent Application No. 60/501,142, filed Sep. 10, 2003, and U.S. Provisional Patent Application No. 60/515,582 filed Oct. 30, 2003, the contents of both of which are incorporated by reference.

FIELD OF THE INVENTION

This invention is related to analysis of laboratory data. More specifically, it relates to a method and system for quantitatively analyzing biological samples.

BACKGROUND OF THE INVENTION

Molecular methods are one standard approach used to diagnose and treat a number of diseases. Microarray based (e.g., High Content Screening (HCS)) analysis High Throughput Screening (HTS) analysis and gene expression profiling studies can be used to identify diagnosis markers, analyze disease progression and aid prognosis in developing cures (e.g., cancer progression and tumor analysis). This results in the availability of new and improved diagnostic tests to patients and physicians.

Such molecular analysis also provides the beginning of personalized medicine. Molecular-based therapies are becoming more targeted, such that knowledge of specific genetic variations present in individual patients guides selection of the best possible therapy, while minimizing chances of adverse reactions. This concept has also found its way to the drug development process, which is likely to result in an increasing number of new therapeutic agents that are prescribed in conjunction with “theranostic” genetic tests that guide use of the therapy in appropriate patient populations.

Gene expression using microarray technology allows the simultaneous assessment of the transcription of tens of thousands of genes, and of their relative expression between normal cells and malignant cells. Tissue Microarray Analysis (TMA) significantly impacts a researcher's ability to explore the genetic changes associated with cancer etiology and development and ultimately lead to the discovery of new biomarkers for disease diagnosis, prognosis and new therapeutic tools. Such onco-analysis at molecular level is much more specific and accurate than in the past.

Also the microarray genetic profile of patients can reveal individual patients response to chemotherapy. Comparative genome hybridization studies based on microarray technology may also predict alterations in cancer patients' genetic profiles. Microarray based expression-profiling studies and its correlation to clinical/histopathological/cystopathological findings still needs to be greatly fine-tuned to increase its significance. More sophisticated and high throughput validation approaches are required to allow the screening of hundreds of potential biomarkers onto thousands of tumor samples.

The use of newer imaging tools such as automated microscopes and digital imaging in the laboratory setting has resulted in a need for more sophisticated diagnostic software tools. Currently histological, cytological and imunohistochemical specimens are stored on glass slides, and these slides are read manually by technicians. Such analysis is, necessarily, based on subjective, human interpretation and is subject to human error. Clinicians and oncologists desire higher level of precision, objectivity, reproducibility and standardization in the pathological results.

Analyzing the tissue samples stained with immunohistochemical (IHC) reagents has been the key development in the practice of pathology. Both normal and diseased cells have certain physical characteristics that can be used to differentiate them. These characteristics include complex patterns, rare events, and subtle variations in color and intensity. These variants are what a pathologist looks for when scanning a slide with a manual microscope.

Hematoxillin and Eosin (H&E) is a method of staining is used to study the morphology of tissue samples. Oncologists attempt to identify particular types of cancer by detecting variations in the patterns from the normal tissue. H&E staining can also be used to determine the pathological grading/staging of cancer (e.g., the Richardson and Bloom Method).

This pathological grading of cancer is important from both a diagnostic and predictive perspective. Currently, pathologists must rely on manually analyzed samples without the benefit of any software tool. It is desirable to provide automated objective and reproducible results with fewer variations from pathologist-pathologist and lab-to-lab.

Until recently, most computer-driven systems could match the human eye in its ability to recognize complex patterns. There are limits, however, to the power of the human eye. Fatigue, repetitiveness of the task, large numbers of cells inside a given sample can be a problem for pathologists, as can the inability to distinguish between similar colors. Also computers typically can surpass the human eye in their ability to detect rare events and recognize subtle variations in color and intensity. Computer scientists first approached the challenge of automating the reading of samples on glass slides from a pattern-recognition angle, attempting to write a software program to duplicate the actions of the human brain.

Looking for a single object within a range of objects of similar but different sizes and orientations is very difficult. Additionally, to find the object when it is partially obscured or broken further increases the complexity. If a human looks at the same scene for approximately 5 to 10 minutes the brain acclimatizes to it and loses the ability to detect subtle changes in the scene. It is as though the observer is seeing a memory, and not the actual scene. Having a person spend a long period of time scanning slides that have similar features leads to fatigue and increases the chance of missing important information in the slides. Computers do not experience this fatigue and so are effective at detecting subtle changes in specific criteria. Therefore, rare-event detection is an appropriate use of a computer's power.

Additionally, because the human brain calculates brightness and color by comparison with the local context, a level of subjectivity is incorporated into reading stained slides manually. Thus, IHC/H&E slides read by manual microscopy are scored only semi quantitatively, in accordance with the subjective and approximate nature of the human eye's color perception.

Based on the foregoing considerations, there is a need for reliable, information driven histopathological and cystopathological image analysis software system for quantification, image management and retrieval based on a self-learning framework using color, intensity, morphological patterns, artificial intelligence and expert system, personalization and remote communications.

Thus, it is desirable to provide a reliable, information driven software system for analyzing tissue and cell samples, which can create, retrieve, manage, and statistically and/or quantitatively analyze such images to increase their diagnostic and predictive value.

SUMMARY OF THE INVENTION

In accordance with preferred embodiments of the invention, some of the problems associated with human interpretation and error associated with manually analyzing images created from biological samples are overcome. A method and system for quantitatively analyzing images created from biological samples is presented.

The method and system includes, but is not limited to, automatically analyzing plural digital images created from plural biological tissue samples to which a staining reagent or a an immunohistochemical (IHC) compound has been applied, automatically quantitatively analyzing the relevant properties of the digital images, and creating interpretive data, images and reports resulting from such analysis.

The foregoing and other features and advantages of preferred embodiments of the present invention will be more readily apparent from the following detailed description. The detailed description proceeds with references to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Preferred embodiments of the present invention are described with reference to the following drawings, wherein:

FIG. 1 is a block diagram illustrating an exemplary biological sample analysis processing system;

FIG. 2 is a block diagram illustrating applications of the biological sample analysis processing system of FIG. 1;

FIG. 3 is a flow diagram illustrating a method for automatically creating a medical diagnosis;

FIG. 4 is a block diagram illustrating a data flow for the method of FIG. 3;

FIG. 5 is a flow diagram illustrating a method for automatically creating a medical diagnosis;

FIG. 6 is a block diagram illustrating a data flow for the method of FIG. 4;

FIG. 7 is a flow diagram illustrating a method completing a pathological diagnosis with a pathiam; and

FIG. 8 is a block diagram illustrating another data flow for analyzing digital images.

DETAILED DESCRIPTION OF THE INVENTION

Exemplary Biological Sample Analysis System

FIG. 1 is a block diagram illustrating an exemplary biological sample analysis processing system 10. The exemplary biological sample analysis processing system 10 includes, but is not limited to, one or more computers 12 with a computer display 14. The computer display 14 presents a windowed graphical user interface (GUI) 16 with multiple windows to a user. The one or more computers 12 may be replaced with client terminals in communications with one or more servers, a personal digital/data assistant (PDA), a laptop computer, a mobile computer, an Internet appliance, one or two-way pagers, or other similar mobile or hand-held electronic device.

The one or more computers are associated with one or more databases 18 (one of which is illustrated) includes biological sample information in various digital image or digital data formats. The one or more databases 18 may be integral to a memory system on the computer 12 or in secondary storage such as a hard disk, floppy disk, optical disk, or other non-volatile mass storage devices. The computer 12 includes one or more applications 20 for analyzing biological samples.

The one or more computers 12 are also in communications with a communications network 22 such as the Internet, an intranet, a Local Area Network (LAN) or other computer network. Functionality of the medical data analyzing system 10 can also be distributed over plural computers 12 via the communications network 22.

The communications network 22 includes, but is not limited to, the Internet, an intranet, a wired Local Area Network (LAN), a wireless LAN (WiLAN), a Wide Area Network (WAN), a Metropolitan Area Network (MAN), Public Switched Telephone Network (PSTN) and other types of communications networks 18 providing voice, video and data communications.

The communications network 22 may include one or more gateways, routers, or bridges. As is known in the art, a gateway connects computer networks using different network protocols and/or operating at different transmission capacities. A router receives transmitted messages and forwards them to their correct destinations over the most efficient available route. A bridge is a device that connects networks using the same communications protocols so that information can be passed from one network device to another.

The communications network 22 may include one or more servers and one or more web-sites accessible by user to send and receive information useable by the one or more computers 12. The communications network 22 includes, but is not limited to data networks using the Transmission Control Protocol (TCP), User Datagram Protocol (UDP), Internet Protocol (IP) and other data protocols.

As is know in the art, TCP provides a connection-oriented, end-to-end reliable protocol designed to fit into a layered hierarchy of protocols which support multi-network applications. TCP provides for reliable inter-process communication between pairs of processes in network devices attached to distinct but interconnected networks. For more information on TCP see Internet Engineering Task Force (ITEF) Request For Comments (RFC)-793, the contents of which are incorporated herein by reference.

As is know in the art, UDP provides a connectionless mode of communications with datagrams in an interconnected set of computer networks. UDP provides a transaction oriented datagram protocol, where delivery and duplicate packet protection are not guaranteed. For more information on UDP see IETF RFC-768, the contents of which incorporated herein by reference.

As is known in the art, IP is an addressing protocol designed to route traffic within a network or between networks. IP is described in IETF Request For Comments (RFC)-791, the contents of which are incorporated herein by reference. However, more fewer or other protocols can also be used on the communications network 28 and the present invention is not limited to TCP/UDP/IP.

The communications network 22 may also include portions of a Public Switched Telephone Network (PSTN) or cable television network (CATV) that connects the one or more computers 12 via one or more twisted pairs of copper wires, coaxial cable, fiber optic cable, other connection media or other connection interfaces with corresponding wired connection protocols (e.g., DSL, ADSL, ISDN, etc.) The PSTN is any public switched telephone network provided by AT&T, GTE, Sprint, MCI, SBC, Verizon and others.

Preferred embodiments of the present invention includes network devices and interfaces that are compliant with all or part of standards proposed by the Institute of Electrical and Electronic Engineers (IEEE), International Telecommunications Union-Telecommunication Standardization Sector (ITU), European Telecommunications Standards Institute (ETSI), Internet Engineering Task Force (IETF), U.S. National Institute of Security Technology (NIST), American National Standard Institute (ANSI), Wireless Application Protocol (WAP) Forum, Data Over Cable Service Interface Specification (DOCSIS) Forum, Bluetooth Forum, or the ADSL Forum. However, network devices and interfaces based on other standards could also be used.

IEEE standards can be found on the World Wide Web at the Universal Resource Locator (URL) “www.ieee.org.” The ITU, (formerly known as the CCITT) standards can be found at the URL “www.itu.ch.” ETSI standards can be found at the URL “www.etsi.org.” IETF standards can be found at the URL “www.ietf.org.” The NIST standards can be found at the URL “www.nist.gov.” The ANSI standards can be found at the URL “www.ansi.org.” The DOCSIS standard can be found at the URL “www.cablemodem.com.” Bluetooth Forum documents can be found at the URL “www.bluetooth.com.” WAP Forum documents can be found at the URL “www.wapforum.org.” ADSL Forum documents can be found at the URL “www.adsl.com.”

The digital images include digital images of biological samples taken via a microscope with a digital camera and stored in a variety of digital image formats including, bit-mapped, joint pictures expert group (JPEG), graphics interchange format (GIF), etc. However, the present invention is not limited to these digital image formats and other digital image or digital data formats can also be used to practice the invention.

The digital images are typically obtained by magnifying the biological samples with a microscope or other magnifying device and capturing a digital image of the magnified biological sample (e.g., groupings of plural magnified cells, etc.).

One embodiment includes a microscope or other magnifying device and a digital or analog camera. Another embodiment optionally includes a microscope with a digital camera 24.

The term “sample” includes cellular material derived from a biological organism. Such samples include but are not limited to hair, skin samples, tissue samples, cultured cells, cultured cell media, and biological fluids. The term “tissue” refers to a mass of connected cells (e.g., CNS tissue, neural tissue, or eye tissue) derived from a human or other animal and includes the connecting material and the liquid material in association with the cells. The term “biological fluid” refers to liquid material derived from a human or other animal. Such biological fluids include, but are not limited to, blood, plasma, serum, serum derivatives, bile, phlegm, saliva, sweat, amniotic fluid, and cerebrospinal fluid (CSF), such as lumbar or ventricular CSF. The term “sample” also includes media containing isolated cells. The quantity of sample required to obtain a reaction may be determined by one skilled in the art by standard laboratory techniques. The optimal quantity of sample may be determined by serial dilution.

An operating environment for the devices biological sample analysis processing system 10 include a processing system with one or more high speed Central Processing Unit(s) (“CPU”), processors and one or more memories. In accordance with the practices of persons skilled in the art of computer programming, the present invention is described below with reference to acts and symbolic representations of operations or instructions that are performed by the processing system, unless indicated otherwise. Such acts and operations or instructions are referred to as being “computer-executed,” “CPU-executed,” or “processor-executed.”

It will be appreciated that acts and symbolically represented operations or instructions include the manipulation of electrical signals or biological signals by the CPU or processor. An electrical system or biological system represents data bits which cause a resulting transformation or reduction of the electrical signals or biological signals, and the maintenance of data bits at memory locations in a memory system to thereby reconfigure or otherwise alter the CPU's or processor's operation, as well as other processing of signals. The memory locations where data bits are maintained are physical locations that have particular electrical, magnetic, optical, or organic properties corresponding to the data bits.

The data bits may also be maintained on a computer readable medium including magnetic disks, optical disks, organic memory, and any other volatile (e.g., Random Access Memory (“RAM”)) or non-volatile (e.g., Read-Only Memory (“ROM”), flash memory, etc.) mass storage system readable by the CPU. The computer readable medium includes cooperating or interconnected computer readable medium, which exist exclusively on the processing system or can be distributed among multiple interconnected processing systems that may be local or remote to the processing system.

The digital images include digital images of biological samples taken with a camera such as a digital camera and stored in a variety of digital image formats including, bit-mapped, joint pictures expert group (JPEG), graphics interchange format (GIF), etc. However, the present invention is not limited to these digital image formats and other digital image or digital data formats can also be used to practice the invention.

The digital images are typically obtained by magnifying the biological samples with a microscope or other magnifying device and capturing a digital image of the magnified biological sample (e.g., groupings of plural magnified cells, etc.).

The term “sample” includes cellular material derived from a biological organism. Such samples include but are not limited to hair, skin samples, tissue samples, cultured cells, cultured cell media, and biological fluids. The term “tissue” refers to a mass of connected cells (e.g., CNS tissue, neural tissue, or eye tissue) derived from a human or other animal and includes the connecting material and the liquid material in association with the cells. The term “biological fluid” refers to liquid material derived from a human or other animal. Such biological fluids include, but are not limited to, blood, plasma, serum, serum derivatives, bile, phlegm, saliva, sweat, amniotic fluid, and cerebrospinal fluid (CSF), such as lumbar or ventricular CSF. The term “sample” also includes media containing isolated cells. The quantity of sample required to obtain a reaction may be determined by one skilled in the art by standard laboratory techniques. The optimal quantity of sample may be determined by serial dilution.

An operating environment for the devices biological sample analysis processing system 10 include a processing system with one or more high speed Central Processing Unit(s) (“CPU”), processors and one or more memories. In accordance with the practices of persons skilled in the art of computer programming, the present invention is described below with reference to acts and symbolic representations of operations or instructions that are performed by the processing system, unless indicated otherwise. Such acts and operations or instructions are referred to as being “computer-executed,” “CPU-executed,” or “processor-executed.”

It will be appreciated that acts and symbolically represented operations or instructions include the manipulation of electrical signals or biological signals by the CPU or processor. An electrical system or biological system represents data bits which cause a resulting transformation or reduction of the electrical signals or biological signals, and the maintenance of data bits at memory locations in a memory system to thereby reconfigure or otherwise alter the CPU's or processor's operation, as well as other processing of signals. The memory locations where data bits are maintained are physical locations that have particular electrical, magnetic, optical, or organic properties corresponding to the data bits.

The data bits may also be maintained on a computer readable medium including magnetic disks, optical disks, organic memory, and any other volatile (e.g., Random Access Memory (“RAM”)) or non-volatile (e.g., Read-Only Memory (“ROM”), flash memory, etc.) mass storage system readable by the CPU. The computer readable medium includes cooperating or interconnected computer readable medium, which exist exclusively on the processing system or can be distributed among multiple interconnected processing systems that may be local or remote to the processing system.

Analyzing Biological Samples to Create a Medical Diagnosis

FIG. 2 is a block diagram 26 illustrating applications 20 of the biological sample analysis processing system 10, that include, but are not limited to a digital image analysis module 28 for automatically analyzing a plural digital images created from a plural biological tissue samples to which an staining reagent has been applied to determine one or more areas of interest and for automatically analyzing a plural digital images created from a plural biological tissues samples to which an immunohistochemical (IHC) compound has been applied to determine one or more areas of interest, a medical analysis module 30 for automatically quantitatively analyzing the one or more determined areas of interest to automatically generate additional interpretive images, medical data, medical statistics or medical reports of predictive value or diagnostic value, a display module 32 to display the plural digital images, the additional interpretive images, the medical data, medical statistics or medical reports on a graphical user interface display, a recorder module 34 to automatically record, store and apply knowledge generated by the quantitatively analysis of the one or more determined areas of interest.

In one embodiment the applications 20 are software applications. However the invention is not limited

FIG. 3 is a flow diagram illustrating a Method 36 for automatically creating a medical diagnosis. At Step 38, plural digital images created from plural biological tissue samples to which a staining reagent has been applied are acquired. At Step 40, the plural digital images are pre-processed to adjust, if necessary, a contrast level and a color level. At Step 42, a histogram analysis is performed on the plural digital images using grey and red, green, blue (RGB) luminosity values to locate one or more areas of interest. At Step 44, one or more areas of interest in which one or more human cancer cells may be present are determined using automatic chromatin pattern analysis, automatic nucleoar pattern analysis or automatic mitotic activity pattern analysis. At Step 46, the determined one or more areas of interest in which one or more human cancer cells may be present are classified and used to create a medical diagnosis.

In one embodiment, Method 36 further comprises creating new medical knowledge using the determined one or more areas of interest and automatically adjusting the processing completed at Steps 40-46 to further refine the ability to automatically detect and classify one or more human cancer cells to create a medical diagnosis.

FIG. 4 is a block diagram 48 illustrating a data flow for Method 36. FIG. 4 illustrates as analysis of digital images are completed new medical diagnostic knowledge is obtained. The new medical diagnostic knowledge is then automatically re-applied to the analysis techniques used to further the further refine the ability to automatically detect and classify one or more cancer cells to create a medical diagnosis.

FIG. 5 is a flow diagram illustrating a Method 50 for automatically creating a medical diagnosis. At Step 52, plural digital images created from plural biological tissue samples to which an immunohistochemical (IHC) compound has been applied are acquired. At Step 54, the plural digital images are pre-processed to adjust, if necessary, a contrast level and a color level. At Step 56, a histogram analysis is performed on the plural digital images using grey and red, green, blue (RGB) luminosity values to locate one or more areas of interest. At Step 58, one or more areas of interest in which one or more cancer cells may be present are determined using automatic nuclear pattern analysis, automatic cytoplasmic pattern analysis or automatic membrane pattern analysis. At Step 60, the determined one or more areas of interest in which one or more cancer cells may be present are classified and used to create a medical diagnosis.

In one embodiment, Method 50 further comprises creating new medical knowledge using the determined one or more areas of interest and automatically adjusting the processing completed at Steps 52-60 to further refine the ability to automatically detect and classify one or more cancer cells to create a medical diagnosis.

FIG. 6 is a block diagram 62 illustrating a data flow for Method 50. FIG. 5 illustrates as analysis of digital images are completed new medical diagnostic knowledge is obtained. The new medical diagnostic knowledge is then automatically re-applied to the analysis techniques used to further the further refine the ability to automatically detect and classify one or more cancer cells to create a medical diagnosis.

A PATHIAM is a tool used by pathologists to assist in quantification and reporting a pathological diagnosis. A PATHIAM automatically uses digital image intensity values and classifies them into useable identifiable patterns to locate biological components such as cell membranes and other shapes decoded as morphological feature patterns and other types of patterns. A PATHIAM is thus used an aid or enabling tool for pathologists and other medical or research professionals to help them in analyzing biological tissue and other biological samples with more precision and accuracy using automated processes.

FIG. 7 is a flow diagram illustrating a Method for 64 completing a pathological diagnosis with a pathiam. At Step 66, plural digital image intensity values from a plural digital images created from plural biological tissue samples to which a staining reagent or an immunohistochemical (IHC) compound has been applied are automatically analyzed to determine one or more areas of interest. At Step 68, the determined one or more areas of interest are automatically classified into one or more patterns used to locate one or more morphological features from the biological tissue samples in which one or more human cancer cells may be present. At Step 70, a pathological diagnosis is automatically created using the classified one or more patterns.

FIG. 8 is a block diagram 72 illustrating another data flow for analyzing digital images. The applications 20 are capable of acquiring images from digital microscopic equipment or from image databases, either managed or unmanaged.

In one embodiment, the applications 20 are based on an expert system framework for analyzing Histopathological and Cytopathological biopsy slides, which may be stained with, for example, Hematoxillin-Eosin or with Imunohistochemical reagents. The stained slides can then subjected to microscopic examinations.

The parameters used for digital image analysis are configurable by a user and default parameters may be modified and refined per set of images processed via self-learning engine collecting knowledge across the framework.

The analyzed digital images also are grouped/clustered/classified based on similar trends and features and each formed cluster can then aid the knowledge extraction step indicated by a possible knowledge base (KB) creation. The KB creation collects information thus received as a part of analysis, which it may correlate to the statistical libraries and other database to generate knowledge rules.

The system includes but is not limited to the steps of creating or capturing images of the cell or tissue or other biological samples. The biological samples may, but are not required to be, treated with chemical reagents to stain or enhance the visibility of certain properties of the biological samples.

The properties of the captured images are quantitatively analyzed by the software to generate additional interpretive images, data and/or reports of predictive and diagnostic value having various content, appearance and format.

Exemplary applications of the invention may include but are not limited to: (1) Oncopathology Diagnosis: a PATHIAM may be useful in Oncopathological or other diagnosis of biopsy slides. For example, digital images of H&E and IHC stained breast biopsy slides can be analyzed using PATHIAM for pathological TNM scoring and common IHC breast panel analysis such as ER, PR, HER-2 positivity tests. These analysis results may be useful not only from diagnostic viewpoint but may have prognostic value associated with it; (2) Oncopathology Research: an immunostain database data with statistical values and tissue microarray features are useful to research community characteristics. For example, a PATHIAM may help the pathologist choose the best panel of immunostains, which may in turn help differentiate between the tumors. This may be a useful application considering the market availability of a large pool of antibody reagents. The knowledge database lists the antibodies that can differentiate between the type of tumors entered by the user (e.g., ductal ca vs. lobular ca). The system may also grade antibodies with respect to their ability to differentiate between the tumors; (3) Cytopathogocial Diagnosis: high power image analysis of H&E stained slides is beneficial for the cytopathologists. Nuclear grading based on nuclear pleomorphism with graphical display 14 and quantitification of chromatin and nucleolar pattern may be of significance in cystopathological diagnosis of surgical/FNA slides. This assists in distinguishing benign and malignant tumors1 (4) Cytopathogocial Research: comparison of graphs and values of nuclear pleomorphism, chromatin and nucleolar pattern with prior cases having similar or closely similar results may assist cytopathology researchers; (5) Histotopathological Diagnosis: A PATHIAM is useful for histopathological or other diagnosis of surgical biopsy slides. The H&E module may help in morphological analysis while IHC marker may be useful for standardization and quantification of biomarker levels on tissue samples. The peer review model is also useful from a histopathological or other diagnostic point of view; (6) Histopathogocial Research: useful features for histopathical research may include image management and retrieval and peer review; (7) Molecular Diagnostics: The system includes functional capabilities such as object identification, object counting, staining-intensity measurement, and morphometric characterization. The functions are applied individually or in combinations to enable licensed laboratory professionals or others to analyze any slides and other media stained with H&E, IHC or ICC reagents, such as those stained for ER/PR, HER2, EGFR, Ki-67, p53, and other stains. PATHIAM will also assist Microarray based expression profiling studies and its correlation to clinical/ histopathological/cytopathological findings, which is the basis of molecular diagnostics. This potentially may allow the screening of hundreds of potential biomarkers onto thousands of tumor or other samples simultaneously; (8) Pharmacogenomics, Drug Discovery, and Clinical Trials: It is anticipated that growth in the field and emergence of advanced bio-informatics tools for data mining and analysis with simpler array designs may facilitate this technology in diagnosis and prognosis of cancers. And very soon the screening tests based on microarray findings may one of the many tests performed on cancer patients in a multidisciplinary team patient management approach to cancer diagnosis and prognosis.

It should be understood that the programs, processes, methods and system described herein are not related or limited to any particular type of computer or network system (hardware or software), unless indicated otherwise. Various combinations of general purpose, specialized or equivalent computer components including hardware, software, and firmware and combinations thereof may be used with or perform operations in accordance with the teachings described herein.

In view of the wide variety of embodiments to which the principles of the present invention can be applied, it should be understood that the illustrated embodiments are exemplary only, and should not be taken as limiting the scope of the present invention. For example, the steps of the flow diagrams may be taken in sequences other than those described, and more fewer or equivalent elements may be used in the block diagrams.

The claims should not be read as limited to the described order or elements unless stated to that effect. In addition, use of the term “means” in any claim is intended to invoke 35 U.S.C. §112, paragraph 6, and any claim without the word “means” is not so intended.

Therefore, all embodiments that come within the scope and spirit of the following claims and equivalents thereto are claimed as the invention.

Referenced by
Citing PatentFiling datePublication dateApplicantTitle
US7941275Mar 27, 2005May 10, 2011Ventana Medical Systems, Inc.Method and system for automated detection of immunohistochemical (IHC) patterns
US7979212Jan 31, 2005Jul 12, 2011Ventana Medical Systems, Inc.Method and system for morphology based mitosis identification and classification of digital images
US8114615May 17, 2007Feb 14, 2012Cernostics, Inc.Method for automated tissue analysis
US8428887Nov 9, 2011Apr 23, 2013Ventana Medical Systems, Inc.Method for automated processing of digital images of tissue micro-arrays (TMA)
US8515683Apr 4, 2011Aug 20, 2013Ventana Medical Systems, Inc.Method and system for automated detection of immunohistochemical (IHC) patterns
US8597899Aug 24, 2012Dec 3, 2013Cernostics, Inc.Method for automated tissue analysis
US8770954Feb 10, 2011Jul 8, 2014KickSmart International, Inc.Human-powered irrigation pump
US8774518Aug 1, 2012Jul 8, 2014Nec Laboratories America, Inc.Digital pathology system with low-latency analytics
US8861810 *Jan 5, 2010Oct 14, 2014Vala Sciences, Inc.Automated image analysis with GUI management and control of a pipeline workflow
US8891851Jul 13, 2010Nov 18, 2014Glenn F. SpauldingHome healthcare management system and hardware
US8897537Aug 1, 2012Nov 25, 2014Nec Laboratories America, Inc.Cloud-based digital pathology
US8934718Aug 1, 2012Jan 13, 2015Nec Laboratories America, Inc.Interactive analytics of digital histology slides
US8943909 *Apr 7, 2010Feb 3, 2015Hitachi High-Technologies CorporationAutomatic analyzer
US20070076929 *Oct 5, 2005Apr 5, 2007General Electric CompanySystem and method for automatic post processing image generation
US20100192084 *Jan 5, 2010Jul 29, 2010Vala Sciences, Inc.Automated image analysis with gui management and control of a pipeline workflow
US20120036944 *Apr 7, 2010Feb 16, 2012Hitachi High-Technologies CorporationAutomatic analyzer
US20120114219 *Jan 17, 2012May 10, 2012Nikon CorporationImage processing apparatus, incubation observing apparatus, and image processing method
US20120207360 *Feb 11, 2011Aug 16, 2012Courosh MehanianSystems and Methods for Object Identification
US20120314059 *Jun 10, 2011Dec 13, 2012Franz-Josef HoffmannMethod for dynamically detecting the fill level of a container, container therefor, and system for dynamically monitoring the fill level of a plurality of containers
US20130004970 *Jul 5, 2012Jan 3, 2013The Regents Of The University Of Colorado, A Body CorporateMethods for prediction of clinical outcome to epidermal growth factor receptor inhibitors by cancer patients
Classifications
U.S. Classification435/40.5, 702/19, 382/128
International ClassificationG06T7/00, G06T5/50, G06T5/40, G06T5/00, C12Q1/00, G01N33/50, G01N33/48, G06F19/00, G06K9/00, G01N33/53, G01N1/30, C12Q1/68
Cooperative ClassificationY10S128/922, G06T2200/24, G06F19/345, G06T7/0081, G01N1/30, G06K9/00127, G01N33/5005, G06T2207/30072, G06K9/48, G06T2207/10056, G06T7/401, G06T7/0012, G06K9/0014, G06T2207/30024, G01N33/5091
European ClassificationG06T7/00B2, G01N33/50D, G06T7/40A, G06K9/00B, G06K9/00B2, G06T7/00S1, G06T5/00D, G06K9/48, G01N33/50D4
Legal Events
DateCodeEventDescription
Mar 24, 2011ASAssignment
Free format text: MERGER;ASSIGNOR:BIOIMAGENE, INC.;REEL/FRAME:026013/0131
Effective date: 20101221
Owner name: VENTANA MEDICAL SYSTEMS, INC., ARIZONA
Mar 8, 2005ASAssignment
Owner name: BIOIMAGENE, INC., CALIFORNIA
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:GHOLAP, GAURI A.;GHOLAP, ABHIJEET S.;REEL/FRAME:016347/0423
Effective date: 20041211