Automatic Detection of Tumor Budding in Colorectal Carcinoma with Deep Learning

J. Bokhorst, L. Rijstenberg, D. Goudkade, I. Nagtegaal, J. van der Laak and F. Ciompi

Computational Pathology and Ophthalmic Medical Image Analysis (2018)

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Abstract

Colorectal cancer patients would benefit from a valid, reliable and efficient detection of Tumor Budding (TB), as this is a proven prognostic biomarker. We explored the application of deep learning techniques to detect TB in Hematoxylin and Eosin (H&E) stained slides, and used convolutional neural networks to classify image patches as containing tumor buds, tumor glands and background. As a reference standard for training we stained slides both with H&E and immunohistochemistry (IHC), where one pathologist first annotated buds in IHC and then transferred the obtained annotations to the corresponding H&E image. We show the effectiveness of the proposed three-class approach, which allows to substantially reduce the amount of false positives, especially when combined with a hard-negative mining technique. Finally we report the results of an observer study aimed at investigating the correlation between pathologists at detecting TB in IHC and H&E.

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