Pancreas Segmentation Based on Two-stage Network of Multiple Attention Mechanisms

doi:10.3969/j.issn.1006-2475.2025.10.011

Abstract

Abstract:
Abstract:Pancreas segmentation is of great significance in computer-aided diagnosis of pancreatic diseases. The pancreas is characterized by small size， large individual differences， and blurred margins， so the task of pancreas segmentation is extremely challenging. To solve the above problems, this paper proposes a new network based on two-stage multi-attention mechanism. Firstly， in order to solve the problem of imbalance between the background and the target， this paper uses a two-stage segmentation method to use the coarse segmentation stage to clip out the candidate region as the input of the fine segmentation stage. Secondly， for the problem of large individual differences in the pancreas， the channel attention mechanism block is designed to the decoder， and the Squeeze and Excited Attention Module（SE Module） is also introduced to adapt to different shapes and sizes of pancreas by using its adaptive attention mechanism. Finally， the Convolutional Attention Module （CBAM） is used to strengthen the information transmission between the encoder and the decoder to improve the segmentation accuracy of the model. The proposed method is tested on the NIH dataset， and the results show that the proposed method has good performance and can effectively solve the problem that the pancreas is difficult to segment in abdominal CT images.

Key words: Key words: pancreas segmentation, two-stage, attention mechanisms, dual-channel attention mechanism block, CBAM

CLC Number:

中图分类号：TP391

ZHOU Bangyuan1, XIN Guojiang1, LIANG Hao2, DING Changsong1. Pancreas Segmentation Based on Two-stage Network of Multiple Attention Mechanisms[J]. Computer and Modernization, 2025, 0(10): 67-72.

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