Harnessing Deep Bladder Tumor Segmentation with Logical Clinical Knowledge

Segmentation of bladder tumors from Magnetic Resonance (MR) images is important for early detection and auxiliary diagnosis of bladder cancer. Deep Convolutional Neural Networks (DCNNs) have been widely used for bladder tumor segmentation but the DCNN-based tumor segmentation over-depends on data training and neglects the clinical knowledge. From a clinical point of view, a bladder tumor must rely on the bladder wall to survive and grow, and the domain prior is very helpful for bladder tumor localization. Aiming at the problem, we propose a novel bladder tumor segmentation method in which the clinical logic rules of bladder tumor and wall are incorporated into DCNNs and make the segmentation of DCNN harnessed by the clinical rules. The logic rules provide a semantic and friendly knowledge representation for human clinicians, which are easy to set and understand. Moreover, fusing the logic rules of clinical knowledge facilitates to reduce the data dependency of the segmentation network and achieve precise segmentation results even with limited labeled training images. Experiments on the bladder MR images from the cooperative hospital validate the effectiveness of the proposed tumor segmentation method.