  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.
