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

• URL: http://arxiv.org/abs/2003.01337v1
• Date: Tue, 3 Mar 2020 05:08:34 GMT
• Title: DDU-Nets: Distributed Dense Model for 3D MRI Brain Tumor Segmentation
• Authors: Hanxiao Zhang, Jingxiong Li, Mali Shen, Yaqi Wang and Guang-Zhong Yang
• Abstract summary: Three patterns of distributed dense connections (DDCs) are proposed to enhance feature reuse and propagation of CNNs. For better detecting and segmenting brain tumors from 3D MR images, CNN-based models embedded with DDCs (DDU-Nets) are trained efficiently from pixel to pixel. The proposed method is evaluated on the BraTS 2019 dataset with results demonstrating the effectiveness of the DDU-Nets.
• Score: 27.547646527286886
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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