Bladder Cancer Multi Class Segmentation In MRI With Pyramid In Pyramid Network in Matlab

Recognition and segmentation of bladder walls and tumour in MRI is essential for bladder cancer diagnosis. In this paper, we propose a novel Pyramid in Pyramid (PiP) fully convolutional neural network to address this problem. A pyramid backbone with lateral connections between encoder and decoder is utilized to segment the bladder wall and tumour at multiple scales and in an end-to-end fashion. To boost the model's capability of extracting multiscale contextual information, a pyramidal atrous convolution block is embedded into the pyramid backbone. We present experimental results to show that the new method outperforms other state-of-the-art models and that the results have a good consistency with that of experienced radiologists.