嵌入压缩采样的腹部CT胰腺分割网络

doi:10.16451/j.cnki.issn1003-6059.202104002

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Abstract Due to the high anatomical variability of pancreas, it is difficult for automated segmentation algorithms to achieve accurate localization of the target. To solve this problem, an encoder-decoder network embedded with compressive sampling is proposed. By training the network in different stages, the segmentation network can cascade the prior knowledge of pancreas location perceived from the label space in the pre-trained stage. Thus, the precise positioning of the pancreas is realized and the consistency between the segmentation result and the label is ensured. The experimental results of pancreas segmentation show that the performance of the proposed network is better.

Key words： Medical Image Pancreas Segmentation Encoder-Decoder Network Compressive Sampling Model

Received: 15 June 2020

ZTFLH:

TP 391

Fund:Major Program of National Natural Science Foundation of China(No.81727802), Young Scientists Fund of National Natural Science Foundation of China (No. 61701404), Natural Science Foundation of Shaanxi Province of China(No.2020JM-438,2019JM-494), The Special Scientific Research Foundation of Shaanxi Provincial Education Department of China(No.17JK0769)

Corresponding Authors: ZHANG Min, Ph.D., associate professor. Her research interests include artificial intelligence, computer vision and medical image processing.

About author:: XU Qiangqiang, master student. His research interests include machine learning and medical image segmentation. ZHANG Min(Corresponding author), Ph.D., associate professor. Her research interests include artificial intelligence, computer vision and medical image processing. REN Fenggang, Ph.D. candidate. His research interests include medical engineering combined with surgical technology innovation and tumor electromagnetic physical ablation technology. LÜ Yi, Ph.D., professor. His research interests include surgical treatment of hepato-biliary-pancreatic tumor. FENG Jun, Ph.D., professor. Her research interests include medical image processing, artificial intelligence, pattern recognition and multimedia system.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202104002 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2021/V34/I4/300

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