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.
Identification of bladder layers from tissue biopsies is the first step towards an accurate diagnosis and prognosis of bladder cancer. We present an automated Bladder Image Analysis System (BLIAS) that can recognize urothelium, lamina propria, and muscularis propria from images of H and E-stained slides of bladder biopsies. Furthermore, we present its clinical application to automate risk stratification of T1 bladder cancer patients based on the depth of lamina propria invasion. The method uses multidimensional scaling and transfer learning in conjunction with convolutional neural networks to identify different bladder layers from H and E images of bladder biopsies. The method was trained and tested on eighty whole slide images of bladder cancer biopsies. Our preliminary findings suggest that the proposed method has good agreement with the pathologist in identification of different bladder layers. Additionally, given a set of tumor nuclei within lamina propria, it has the potential to risk stratify T1 bladder cancer by computing the distance from this set to urothelium and muscularis propria. Our results suggest that a pretrained network trained via transfer learning is better in identifying bladder layers than a conventional deep learning paradigm.
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