Abstract
BackgroundIdentification 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.MethodsSegmentation 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.ResultsThe 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 independent set of 53 whole slide images and segmented them using our method. Visual examination by a different experienced pathologist yielded an average score of 8.87 ± 0.63 out of 10 for segmentation accuracy.ConclusionsOur preliminary findings suggest that predictions of our model can minimize the time needed by pathologists to annotate slides. Moreover, the method has the potential to identify the bladder layers accurately. Further development can assist the pathologist with the diagnosis of T1 bladder cancer.
Highlights
Bladder cancer remains a prevalent disease in the US
* Correspondence: ttavolar@wakehealth.edu 1Center for Biomedical Informatics, Wake Forest School of Medicine, Winston-Salem, NC, USA Full list of author information is available at the end of the article. Those with an increased depth of lamina propria invasion or with extensive lamina propria invasion are more than three times more likely to progress than patients with “superficial” invasion and have more than twice the risk of cancer-specific mortality [2, 3]
Regions of each slide in S1 were annotated by an in-house pathologist, including urothelium, lamina propria, muscularis propria, red blood cells (RBCs), cauterized tissue, inflammation, and muscularis mucosa
Summary
Bladder cancer remains a prevalent disease in the US. In 2020, an estimated 62,100 men and 19,300 women will be diagnosed with the disease, and another 17,670 individuals are expected to die from it [1]. The five-year recurrence and progression rates of patients with the T1 disease are high at 42% and 20–40%, respectively [2] Those with an increased depth of lamina propria invasion or with extensive lamina propria invasion are more than three times more likely to progress than patients with “superficial” invasion and have more than twice the risk of cancer-specific mortality [2, 3]. The pathologist gave a score of 8.9 ± 0.6 for segmentation accuracy It only took 23 min for the pathologist to assess 15 slides. As the segmentation results on S2 were evaluated by the pathologist who originally annotated the images in S1, there is the possibility of an element of “evaluation bias.”. As the pathologists were reviewing slides that were already annotated by the method, there still exists an element of bias (i.e. the model biases the pathologists)
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