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arXiv 2003.013372020-03-03eess.IVcs.CV

DDU-Nets: Distributed Dense Model for 3D MRI Brain Tumor Segmentation

The Institute of Medical Robotics, Shanghai Jiao Tong University（上海交通大学医疗机器人研究所）；The Hamlyn Centre for Robotic Surgery, Imperial College London（帝国理工学院哈姆林机器人手术中心）；Queen Mary University of London（伦敦玛丽女王大学）；Hangzhou Dianzi University（杭州电子科技大学）

Hanxiao Zhang, Jingxiong Li, Mali Shen, Yaqi Wang, Guang-Zhong Yang

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Segmentation of brain tumors and their subregions remains a challenging task due to their weak features and deformable shapes. In this paper, three patterns (cross-skip, skip-1 and skip-2) of distributed dense connections (DDCs) are proposed to enhance feature reuse and propagation of CNNs by constructing tunnels between key layers of the network. For better detecting and segmenting brain tumors from multi-modal 3D MR images, CNN-based models embedded with DDCs (DDU-Nets) are trained efficiently from pixel to pixel with a limited number of parameters. Postprocessing is then applied to refine the segmentation results by reducing the false-positive samples. The proposed method is evaluated on the BraTS 2019 dataset with results demonstrating the effectiveness of the DDU-Nets while requiring less computational cost.

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