MFSL-Net: A Modality Fusion and Shape Learning based Cascaded Network for Prostate Tumor Segmentation
Fan Zhang, Bo Zhang, Zheng Zhang, Yue Mi, Jingyun Wu, Haiwen Huang, Xirong Que, Wendong Wang · 2021 IEEE International Conference on Big Data (Big Data) · 2021
Contouring prostate tumor in magnetic resonance images is a prerequisite for diagnosis. Automatically segmenting blurred lesion regions is challenging and requires fully leveraging multi-parameter MR images. This paper proposes MFSL-Net, an end-to-end network that cascades two novel sub-networks: 1) a modality fusion network that selectively fuses information of two MRI modalities by expanding a dual-stream CNN with spatial and channel attention modules; 2) a shape learning network that integrates shape learning and context learning to recognize the shape and edge information while preserving high-resolution semantic information. We justify MFSL-Net’s design by ablation experiments and compare its performance with the state-of-the-art approaches. Experimental results show a 3.6% improvement in Dice Similarity Coefficient, which confirms the effectiveness of MFSL-Net.