Involving logical clinical knowledge into deep neural networks to improve bladder tumor segmentation.

Segmentation of bladder tumors from medical radiographic images is of great significance for early detection, diagnosis and prognosis evaluation of bladder cancer. Deep Convolution Neural Networks (DCNNs) have been successfully used for bladder tumor segmentation, but the segmentation based on DCNN is data-hungry for model training and ignores clinical knowledge. From the clinical view, bladder tumors originate from the mucosal surface of bladder and must rely on the bladder wall to survive and grow. This clinical knowledge of tumor location is helpful to improve the bladder tumor segmentation. To achieve this, we propose a novel bladder tumor segmentation method, which incorporates the clinical logic rules of bladder tumor and bladder wall into DCNNs to harness the tumor segmentation. Clinical logical rules provide a semantic and human-readable knowledge representation and are easy for knowledge acquisition from clinicians. In addition, incorporating logical rules of clinical knowledge helps to reduce the data dependency of the segmentation network, and enables precise segmentation results even with limited number of annotated images. Experiments on bladder MR images collected from the collaborating hospital validate the effectiveness of the proposed bladder tumor segmentation method.