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Proceedings Paper

Semantic segmentation for prostate cancer grading by convolutional neural networks
Author(s): Nathan Ing; Zhaoxuan Ma; Jiayun Li; Hootan Salemi; Corey Arnold; Beatrice S. Knudsen; Arkadiusz Gertych
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Paper Abstract

Certain pathology workflows, such as classification and grading of prostate adenocarcinoma according to the Gleason grade scheme, stand to gain speed and objectivity by incorporating contemporary digital image analysis methods. We compiled a dataset of 513 high resolution image tiles from primary prostate adenocarcinoma wherein individual glands and stroma were demarcated and graded by hand. With this unique dataset, we tested four Convolutional Neural Network architectures including FCN-8s, two SegNet variants, and multi-scale U-Net for performance in semantic segmentation of high- and low-grade tumors. In a 5-fold cross-validation experiment, the FCN-8s architecture achieved a mIOU of 0.759 and an accuracy of 0.87, while the less complex U-Net architecture achieved a mIOU of 0.738 and accuracy of 0.885. The FCN-8s architecture applied to whole slide images not used for training achieved a mIOU of 0.857 in annotated tumor foci with a multiresolution processing time averaging 11 minutes per slide. The three architectures tested on whole slides all achieved areas under the Receiver Operating Characteristic curve near 1, strongly demonstrating the suitability of semantic segmentation Convolutional Neural Networks for detecting and grading prostate cancer foci in radical prostatectomies.

Paper Details

Date Published: 6 March 2018
PDF: 13 pages
Proc. SPIE 10581, Medical Imaging 2018: Digital Pathology, 105811B (6 March 2018); doi: 10.1117/12.2293000
Show Author Affiliations
Nathan Ing, Cedars-Sinai Medical Ctr. (United States)
Zhaoxuan Ma, Cedars Sinai Medical Ctr. (United States)
Jiayun Li, Univ. of California, Los Angeles (United States)
Hootan Salemi, Cedars-Sinai Medical Ctr. (United States)
Corey Arnold, Univ. of California, Los Angeles (United States)
Beatrice S. Knudsen, Cedars-Sinai Medical Ctr. (United States)
Arkadiusz Gertych, Cedars-Sinai Medical Ctr. (United States)

Published in SPIE Proceedings Vol. 10581:
Medical Imaging 2018: Digital Pathology
John E. Tomaszewski; Metin N. Gurcan, Editor(s)

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