3D Multi-Organ and Tumor Segmentation Based on Re-Parameterize Diverse Experts

Ping Liu, Chunbin Gu, Bian Wu, Xiangyun Liao, Yinling Qian () and Guangyong Chen ()
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Ping Liu: Zhejiang Laboratory, Hangzhou 311121, China
Chunbin Gu: Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hongkong 999077, China
Bian Wu: Zhejiang Laboratory, Hangzhou 311121, China
Xiangyun Liao: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Yinling Qian: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Guangyong Chen: Zhejiang Laboratory, Hangzhou 311121, China

Mathematics, 2023, vol. 11, issue 23, 1-13

Abstract: Automated segmentation of abdominal organs and tumors in medical images is a challenging yet essential task in medical image analysis. Deep learning has shown excellent performance in many medical image segmentation tasks, but most prior efforts were fragmented, addressing individual organ and tumor segmentation tasks with specialized networks. To tackle the challenges of abdominal organ and tumor segmentation using partially labeled datasets, we introduce Re-parameterizing Mixture-of-Diverse-Experts (RepMode) to abdominal organ and tumor segmentation. Within the RepMode framework, the Mixture-of-Diverse-Experts (MoDE) block forms the foundation, learning generalized parameters applicable across all tasks. We seamlessly integrate the MoDE block into a U-shaped network with dynamic heads, addressing multi-scale challenges by dynamically combining experts with varying receptive fields for each organ and tumor. Our framework incorporates task encoding in both the encoder–decoder section and the segmentation head, enabling the network to adapt throughout the entire system based on task-related information. We evaluate our approach on the multi-organ and tumor segmentation (MOTS) dataset. Experiments show that DoDRepNet outperforms previous methods, including multi-head networks and single-network approaches, giving a highly competitive performance compared with the original single network with dynamic heads. DoDRepNet offers a promising approach to address the complexities of abdominal organ and tumor segmentation using partially labeled datasets, enhancing segmentation accuracy and robustness.

Keywords: multi-organ segmentation; re-parameterize network; partially labeled dataset (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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