Automated detection of diagnostically relevant regions in H&E stained digital pathology slides

Claus Bahlmann; Amar Patel; Jeffrey Johnson; Jie Ni; Andrei Chekkoury; Parmeshwar Khurd; Ali Kamen; Leo Grady; Elizabeth Krupinski; Anna Graham; Ronald Weinstein

doi:10.1117/12.912484

23 February 2012 Automated detection of diagnostically relevant regions in H&E stained digital pathology slides

Claus Bahlmann, Amar Patel, Jeffrey Johnson, Jie Ni, Andrei Chekkoury, Parmeshwar Khurd, Ali Kamen, Leo Grady, Elizabeth Krupinski, Anna Graham, Ronald Weinstein

Author Affiliations +

Proceedings Volume 8315, Medical Imaging 2012: Computer-Aided Diagnosis; 831504 (2012) https://doi.org/10.1117/12.912484
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

We present a computationally efficient method for analyzing H&E stained digital pathology slides with the objective of discriminating diagnostically relevant vs. irrelevant regions. Such technology is useful for several applications: (1) It can speed up computer aided diagnosis (CAD) for histopathology based cancer detection and grading by an order of magnitude through a triage-like preprocessing and pruning. (2) It can improve the response time for an interactive digital pathology workstation (which is usually dealing with several GByte digital pathology slides), e.g., through controlling adaptive compression or prioritization algorithms. (3) It can support the detection and grading workflow for expert pathologists in a semi-automated diagnosis, hereby increasing throughput and accuracy. At the core of the presented method is the statistical characterization of tissue components that are indicative for the pathologist's decision about malignancy vs. benignity, such as, nuclei, tubules, cytoplasm, etc. In order to allow for effective yet computationally efficient processing, we propose visual descriptors that capture the distribution of color intensities observed for nuclei and cytoplasm. Discrimination between statistics of relevant vs. irrelevant regions is learned from annotated data, and inference is performed via linear classification. We validate the proposed method both qualitatively and quantitatively. Experiments show a cross validation error rate of 1.4%. We further show that the proposed method can prune ≈90% of the area of pathological slides while maintaining 100% of all relevant information, which allows for a speedup of a factor of 10 for CAD systems.

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Claus Bahlmann, Amar Patel, Jeffrey Johnson, Jie Ni, Andrei Chekkoury, Parmeshwar Khurd, Ali Kamen, Leo Grady, Elizabeth Krupinski, Anna Graham, and Ronald Weinstein "Automated detection of diagnostically relevant regions in H&E stained digital pathology slides", Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 831504 (23 February 2012); https://doi.org/10.1117/12.912484

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