Systems and methods for automated diagnosis and decision support for breast imaging

• US 20050049497A1
• Filed: 06/25/2004
• Published: 03/03/2005
• Est. Priority Date: 06/25/2003
• Status: Active Grant

• Current Assignee: Siemens Medical Solutions USA Incorporated (Siemens AG)
• Original Assignee: Siemens Medical Solutions USA Incorporated (Siemens AG)
• Inventors: Krishnan, Sriram, Fung, Glenn, Rao, R. Bharat, Dundar, Murat
• Granted Patent: US 7,640,051 B2
• Time in Patent Office: Days
• US Class Current: 600/437
• CPC Class Codes: G06T 2207/10088 Magnetic resonance imaging [MRI]
• CPC Class Codes: G06T 2207/10116 X-ray image
• CPC Class Codes: G06T 2207/20084 Artificial neural networks [ANN]
• CPC Class Codes: G06T 2207/30068 Mammography; Breast
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G16H 30/20 for handling medical images, e.g. DICOM, HL7 or PACS
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: Y10S 128/92 Computer assisted medical diagnostics
• CPC Class Codes: Y10S 128/922 including image analysis
• CPC Class Codes: Y10S 128/923 by comparison of patient data to other data
• CPC Class Codes: Y10S 128/924 using artificial intelligence

Abstract

CAD (computer-aided diagnosis) systems and applications for breast imaging are provided, which implement methods to automatically extract and analyze features from a collection of patient information (including image data and/or non-image data) of a subject patient, to provide decision support for various aspects of physician workflow including, for example, automated diagnosis of breast cancer other automated decision support functions that enable decision support for, e.g., screening and staging for breast cancer. The CAD systems implement machine-learning techniques that use a set of training data obtained (learned) from a database of labeled patient cases in one or more relevant clinical domains and/or expert interpretations of such data to enable the CAD systems to “learn” to analyze patient data and make proper diagnostic assessments and decisions for assisting physician workflow.

Claims

1. A method for providing automatic diagnosis and decision support for breast imaging, comprising:

obtaining information from image data of abnormal breast tissue of a subject patient;

obtaining information from non-image data records of the subject patient; and

automatically diagnosing the abnormal breast tissue using the obtained information.

2. The method of claim 1, wherein obtaining information from image data comprises automatically extracting one or more features from ultrasound image data, which are to be used for automatically diagnosing the abnormal breast tissue.

3. The method of claim 2, wherein the ultrasound data is 3D ultrasound data.

4. The method of claim 2, further comprising automatically detecting the abnormal breast tissue in the ultrasound image data prior to automatically extracting.

5. The method of claim 2, wherein automatically extracting one or more features from ultrasound image data comprises extracting features related to one of spiculation, acoustic shadowing, or height/depth ratio of the abnormal breast tissue, and any combination of said features.

6. The method of claim 2, wherein automatically extracting one or more features from ultrasound image data comprises extracting features related to one of acoustic, elastic, perfusion, or 3D features of the abnormal breast tissue, or any combination of said features.

7. The method of claim 2, wherein the image data comprises one of ultrasound image data, X-ray mammography image data, MRI image data, and a combination thereof.

8. The method of claim 1, wherein obtaining information from non-image data records comprises automatically extracting features from structured and/or unstructured data sources comprising clinical, family and/or history information for the subject patient.

9. The method of claim 1, wherein automatically diagnosing the abnormal breast tissue comprises automatically determining a probability of diagnosis.

10. The method of claim 9, further comprising automatically determining a confidence of said probability of diagnosis.

11. The method of claim 10, further comprising automatically determining additional information which would increase the confidence of said probability of diagnosis.

12. The method of claim 11, wherein automatically determining additional information further comprises determining a measure of usefulness of said additional information in increasing said confidence of diagnosis.

13. The method of claim 1, further comprising automatically identifying one or more previously diagnosed cases that are similar to a current diagnosis.

14. The method of claim 13, wherein automatically identifying one or more previously diagnosed cases that are similar to a current diagnosis comprises using the obtained information to search a library of labeled cases with features similar to the obtained information.

15. The method of claim 14, comprising displaying the identified cases.

16. The method of claim 1, wherein automatically diagnosing the abnormal breast tissue using the obtained information comprises classifying the abnormal breast tissue using a machine learning method, a model-based method, or any combination of machine learning and model-based methods, that is trained to analyze the obtained information.

17. The method of claim 16, comprising retraining the method for classifying on a continual or periodic basis using expert data and/or data learned from a plurality of case studies.

18. A method for providing automatic diagnosis and decision support for breast imaging, comprising:

obtaining information from ultrasound image data of abnormal breast tissue of a subject patient;

automatically extracting features from the ultrasound image data, the extracted features comprising at least two of strain, fluid motion such as acoustic streaming, blood motion such as contrast perfusion, and B-mode images; and

automatically diagnosing the abnormal breast tissue using the extracted features.

19. The method of claim 18, wherein the ultrasound image data comprises 3D ultrasound image data and further comprising extracting features from the 3D ultrasound image data.

20. A method for providing automated diagnostic and decision support for medical imaging, comprising:

automatically extracting features from patient data of a subject patient, the patient data comprising image data and non-image data; and

automatically determining a current state of the subject patient by analyzing the features extracted from the patient data of the subject patient; and

automatically providing decision support to assist physician workflow regarding a healthcare or diagnostic or therapeutic path for the subject patient, based on a determined current state of the subject patient.

21. The method of claim 20, wherein automatically determining a current state of the subject patient comprises automatically determining a probability of diagnosis of breast cancer or the probability of developing breast cancer in the future.

22. The method of claim 21, wherein automatically providing decision support comprises automatically determining one or more additional features which would increase a confidence of said probability of diagnosis of breast cancer.

23. The method of claim 22, wherein automatically determining one or more additional features further comprises determining for each of said one or more additional features, a measure of usefulness in increasing said confidence of diagnosis.

24. The method of claim 21, wherein automatically providing decision support comprises automatically determining one or more additional imaging methods that would increase a confidence of said probability of diagnosis of breast cancer.

25. The method of claim 24, wherein automatically determining one or more additional imaging methods further comprises determining for each of said one or more additional imaging methods, a measure of usefulness in increasing said confidence of diagnosis.

26. The method of claim 21, wherein automatically providing decision support comprises automatically identifying one or more previously diagnosed cases that are similar to the current case.

27. The method of claim 26, comprising displaying the identified cases.

28. The method of claim 20, wherein automatically determining a current state of the subject patient comprises automatically determining a likelihood of the subject patient developing breast cancer.

29. The method of claim 20, wherein automatically providing decision support comprises automatically determining one or more diagnostic imaging screening tests that could be given to the subject patient for detecting breast cancer or provide a better assessment of the likelihood of the subject patient developing breast cancer.

30. The method of claim 20, wherein automatically determining a current state of the subject patient comprises automatically assessing characteristics and location of a breast tumor.

31. The method of claim 30, wherein automatically providing decision support comprises automatically determining one or more diagnostic imaging tests that could be used for staging the lesion.

32. The method of claim 31, wherein automatically determining one or more diagnostic imaging tests that could be used for staging the lesion further comprises providing a measure of usefulness for each of the diagnostic imaging tests.

33. The method of claim 31, wherein automatically providing decision support comprises automatically identifying one or more previous staging cases that are similar to a current case.

34. The method of claim 33, further comprising displaying the identified staging cases.

35. The method of claim 20, wherein automatically providing decision support to assist physician workflow comprises suggesting a therapy.

36. The method of claim 35, further comprising automatically providing a probability and/or measure of confidence that the therapy will have a determined outcome.

37. The method of claim 35, further comprising automatically providing a probability and/or measure of confidence that the therapy will not have a determined detrimental impact such as side effects.

38. The method of claim 36, wherein the probability is a distribution over possible outcomes both beneficial and detrimental.

39. The method of claim 38, wherein the probability is a set of distributions over possible outcomes both beneficial and detrimental at one or more time points in the future.

40. The method of claim 38, wherein the probability is a time-varying distribution over possible outcomes at different times in the future.