Semantic segmentation to identify bladder layers from H&E Images.

Diagnostic Pathology
Muhammad Khalid Khan NiaziMetin N Gurcan

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 recognizes urothelium, lamina propria, muscularis propria, and muscularis mucosa layers as well as regions of red blood cells, cauterized tissue, and inflamed tissue from images of hematoxylin and eosin stained slides of bladder biopsies. Segmentation is carried out using a U-Net architecture. The number of layers was either, eight, ten, or twelve and combined with a weight initializers of He uniform, He normal, Glorot uniform, and Glorot normal. The most optimal of these parameters was found by through a seven-fold training, validation, and testing of a dataset of 39 whole slide images of T1 bladder biopsies. The most optimal model was a twelve layer U-net using He normal initializer. Initial visual evaluation by an experienced pathologist on an independent set of 15 slides segmented by our method yielded an average score of 8.93 ± 0.6 out of 10 for segmentation accuracy. It took only 23 min for the pathologist to review 15 slides (1.53 min/slide) with the computer annotations. To assess the generalizability of the proposed model, we acquired an additional independ...Continue Reading

References

Nov 1, 2011·European Urology·Bas W G van RhijnAlexandre R Zlotta
Aug 6, 2013·The Journal of Urology·Nihal E MohamedMichael A Diefenbach
Jan 7, 2015·Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology·William Martin-DoyleJoaquim Bellmunt
Aug 6, 2016·Current Urology Reports·Rajan VeeratterapillayChristian Bach
Nov 9, 2017·Nature·Chris Berdik
May 3, 2019·The Lancet Oncology·Muhammad Khalid Khan NiaziMetin N Gurcan
Jan 9, 2020·CA: a Cancer Journal for Clinicians·Rebecca L SiegelAhmedin Jemal

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Methods Mentioned

BETA
biopsies

Software Mentioned

Net
Inception
Tensorflow
Adam

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