基于三路径网络的医学图像分割方法

doi:10.16451/j.cnki.issn1003-6059.202401001

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Abstract Convolutional neural networks make certain progress in medical image segmentation tasks due to their powerful feature extraction capabilities. However, the accuracy of edge segmentation still needs to be improved. To address this problem, a triplet-path network based on edge selection graph reasoning is proposed in this paper, including the target localization path, edge selection path and refinement path. In the target localization path, a multi-scale feature fusion module is designed to aggregate high-level features for the localization of lesion regions. In the edge selection path, an edge-selective graph reasoning module is constructed for edge screening of low-level features and graph reasoning to ensure the edge shape of the relevant lesion region. In the refinement path, a progressive group level refinement module is established to refine the structure information and details of different scale features. Moreover, a composite loss fusing weighted Focal Tversky loss and a weighted intersection over union loss is introduced to mitigate the effects of class imbalance. Experimental results on public datasets demonstrate the superior performance of the proposed method.

Key words： Graph Neural Networks Medical Image Segmentation Deep Learning Edge Learning

Received: 02 November 2023

ZTFLH:

TP391

Fund:National Natural Science Foundation of China(No.62006215,62176244)

Corresponding Authors: YE Hailiang, Ph.D., lecturer. His research interests include deep learning, graph neural networks, image processing and point cloud analysis.

About author:: CAO Feilong, Ph.D., professor. His research interests include deep learning and image processing.JIANG Qingting, Master student. Her research interests include deep learning, graph neural networks and medical image proce-ssing.

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	JIANG Qingting
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Cite this article:

JIANG Qingting,YE Hailiang,CAO Feilong. Medical Image Segmentation Method with Triplet-Path Network[J]. Pattern Recognition and Artificial Intelligence, 2024, 37(1): 1-12.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202401001 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2024/V37/I1/1

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