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Proceedings Paper

Identifying bladder layers from H and E images using U-Net image segmentation
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Paper Abstract

Identification of bladder layers is a necessary prerequisite to bladder cancer diagnosis and prognosis. We present a method of multi-class image segmentation, which can recognize Urothelium, Lamina Propria, Muscularis Propria, and Muscularis Mucosa regions from images of H&E-stained slides of bladder biopsies. This method can also recognize the difference between these layers, and regions of red blood cells, cauterized tissue, and inflamed tissue at pixel level. The segmentation is done using the U-Net deep learning paradigm, which consists of a combination of convolutional neural layers and upscaling layers to encode features from an image. The most optimal model for this task was found by training four different weight initializers and three different U-Nets of varying size and dropout on 39 whole slide images of T1 bladder biopsies. The model was visually evaluated by an experienced pathologist on an independent set of 15 slides. The pathologist gave an average score of 8.93 out of 10 for the segmentation accuracy. It only took 23 mins for the pathologist to review 15 slides. Our preliminary findings suggest that predictions of our model can minimize the time needed by pathologists to review the slides. Moreover, the method has the potential to identify the bladder layers accurately and hence can assist the pathologist with the diagnosis of T1 bladder cancer.

Paper Details

Date Published: 16 March 2020
PDF: 8 pages
Proc. SPIE 11320, Medical Imaging 2020: Digital Pathology, 1132006 (16 March 2020);
Show Author Affiliations
Muhammad Khalid Khan Niazi, Wake Forest School of Medicine (United States)
Enes Yazgan, Wake Forest School of Medicine (United States)
Cheryl Lee, The Ohio State Univ. (United States)
Anil Parwani, The Ohio State Univ. (United States)
Metin N. Gurcan, Wake Forest School of Medicine (United States)

Published in SPIE Proceedings Vol. 11320:
Medical Imaging 2020: Digital Pathology
John E. Tomaszewski; Aaron D. Ward, Editor(s)

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