2021/01/16 by Mark Bandyk, Dheeraj Reddy Gopireddy, Bandyk, Mark G. +7
Medicine · #Bladder and Urothelial Cancer Treatments #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Urinary and Genital Oncology Studies #Urological Disorders and Treatments
paper · pdf · doi:10.48550/arxiv.2101.06498
openalex publication_date 2021/01/16 · openalex created_date 2021/02/01 · openalex updated_date 2026/07/28
Precise determination and assessment of bladder cancer (BC) extent of muscle invasion involvement guides proper risk stratification and personalized therapy selection. In this context, segmentation of both bladder walls and cancer are of pivotal importance, as it provides invaluable information to stage the primary tumour. Hence, multi region segmentation on patients presenting with symptoms of bladder tumours using deep learning heralds a new level of staging accuracy and prediction of the biologic behaviour of the tumour. Nevertheless, despite the success of these models in other medical problems, progress in multi region bladder segmentation is still at a nascent stage, with just a handful of works tackling a multi region scenario. Furthermore, most existing approaches systematically follow prior literature in other clinical problems, without casting a doubt on the validity of these methods on bladder segmentation, which may present different challenges. Inspired by this, we provide an in-depth look at bladder cancer segmentation using deep learning models. The critical determinants for accurate differentiation of muscle invasive disease, current status of deep learning based bladder segmentation, lessons and limitations of prior work are highlighted.