Bladder Wall Segmentation in MRI Images via Deep Learning and Anatomical Constraints

Segmenting the bladder wall from MRI images is of great significance for the early detection and auxiliary diagnosis of bladder tumors. However, automatic bladder wall segmentation is challenging due to weak boundaries and diverse shapes of bladders. Level-set-based methods have been applied to this task by utilizing the shape prior of bladders. However, it is a complex operation to adjust multiple parameters manually, and to select suitable hand-crafted features. In this paper, we propose an automatic method for the task based on deep learning and anatomical constraints. First, the autoencoder is used to model anatomical and semantic information of bladder walls by extracting their low dimensional feature representations from both MRI images and label images. Then as the constraint, such priors are incorporated into the modified residual network so as to generate more plausible segmentation results. Experiments on 1092 MRI images shows that the proposed method can generate more accurate and reliable results comparing with related works, with a dice similarity coefficient (DSC) of 85.48%.