A 3D CNN with a Learnable Adaptive Shape Prior for Accurate Segmentation of Bladder Wall Using MR Images

A 3D deep learning-based convolution neural network (CNN) is developed for accurate segmentation of pathological bladder (both wall border and pathology) using T2-weighted magnetic resonance imaging (T2W-MRI). Our system starts with a preprocessing step for data normalization to a unique space and extraction of a region-of-interest (ROI). The major stage utilizes a 3D CNN for pathological bladder segmentation, which contains a network, called CNN1, that aims to segment the bladder wall (BW) with pathology. However, due to the similar visual appearance of BW and pathology, the CNN1 can not separate them. Thus, we developed another network (CNN2) with an additional pathway to extract BW only. The second pathway in CNN2 is fed with a 3D learnable adaptive shape prior model. To remove noisy and scattered predictions, the networks' soft outputs are refined using a fully connected conditional random field. Our framework achieved accurate segmentation results for the BW and tumor as documented by the Dice similarity coefficient and Hausdorff distance. Moreover, comparative results against the other segmentation approach documented the superiority of our framework to provide accurate results for pathological BW segmentation.