Multi-Slice Dense-Sparse Learning for Efficient Liver and Tumor Segmentation

• URL: http://arxiv.org/abs/2108.06761v1
• Date: Sun, 15 Aug 2021 15:29:48 GMT
• Title: Multi-Slice Dense-Sparse Learning for Efficient Liver and Tumor Segmentation
• Authors: Ziyuan Zhao, Zeyu Ma, Yanjie Liu, Zeng Zeng, Pierce KH Chow
• Abstract summary: Deep convolutional neural network (DCNNs) has obtained tremendous success in 2D and 3D medical image segmentation. We propose a novel dense-sparse training flow from a data perspective, in which, densely adjacent slices and sparsely adjacent slices are extracted as inputs for regularizing DCNNs. We also design a 2.5D light-weight nnU-Net from a network perspective, in which, depthwise separable convolutions are adopted to improve the efficiency.
• Score: 4.150096314396549
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate automatic liver and tumor segmentation plays a vital role in treatment planning and disease monitoring. Recently, deep convolutional neural network (DCNNs) has obtained tremendous success in 2D and 3D medical image segmentation. However, 2D DCNNs cannot fully leverage the inter-slice information, while 3D DCNNs are computationally expensive and memory intensive. To address these issues, we first propose a novel dense-sparse training flow from a data perspective, in which, densely adjacent slices and sparsely adjacent slices are extracted as inputs for regularizing DCNNs, thereby improving the model performance. Moreover, we design a 2.5D light-weight nnU-Net from a network perspective, in which, depthwise separable convolutions are adopted to improve the efficiency. Extensive experiments on the LiTS dataset have demonstrated the superiority of the proposed method.

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