基于深度关联机制的肝胆管超分辨率分割

郑煜; 蔡念; 欧阳文生; 谢依颖; 王平

doi:10.12052/gdutxb.220197

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基于深度关联机制的肝胆管超分辨率分割

计算机科学与技术 | 更新时间：2026-05-27

- 基于深度关联机制的肝胆管超分辨率分割
- Super-resolution Segmentation of Hepatobiliary Ducts Based on Deep Correlation Mechanism
- 广东工业大学学报 2023年40卷第5期页码：41-46
- 作者机构：
  
  1. 广东工业大学信息工程学院,广东,广州,510006
  2. 广州医科大学附属第一医院肝胆外科,广东,广州,510120
- 作者简介：
  
  郑煜 (1998–)，男，硕士研究生，主要研究方向为深度学习、医学图像处理等
  蔡念 (1976–)，男，教授，博士生导师，主要研究方向为机器视觉、机器学习、数字信号处理等，E-mail: cainian@sina.com
- 基金信息：
  
  国家自然科学基金资助项目 (82172019);广州市科技计划项目 (202102010251)
- DOI：10.12052/gdutxb.220197
  中图分类号： TP391
- 网络首发：2023-09-25，
  
  纸质出版：2023
- 稿件说明：
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郑煜, 蔡念, 欧阳文生, 谢依颖, 王平. 基于深度关联机制的肝胆管超分辨率分割[J]. 广东工业大学学报, 2023, 40(5): 41-46.

Zheng Yu, Cai Nian, Ouyang Wen-sheng, et al. Super-resolution Segmentation of Hepatobiliary Ducts Based on Deep Correlation Mechanism[J]. 2023, 40(5): 41-46.
郑煜, 蔡念, 欧阳文生, 谢依颖, 王平. 基于深度关联机制的肝胆管超分辨率分割[J]. 广东工业大学学报, 2023, 40(5): 41-46. DOI： 10.12052/gdutxb.220197.

Zheng Yu, Cai Nian, Ouyang Wen-sheng, et al. Super-resolution Segmentation of Hepatobiliary Ducts Based on Deep Correlation Mechanism[J]. 2023, 40(5): 41-46. DOI： 10.12052/gdutxb.220197.

摘要

肝胆管结石是常见的肝脏疾病，已成为我国非肿瘤性胆道疾病死亡的主要原因，实现对肝胆管层间插值分割重建具有重要意义。针对肝胆管等此类树状型组织器官在分割重建过程中出现断层、不连续等现象，本文提出一种基于深度关联机制的肝胆管CT(Computed Tomography)层间超分辨率分割的端到端级联框架，将层间插值网络和分割网络级联起来进行端到端训练，引入ConvLSTM来加强切片间肝胆管的高维特征信息提取，提出一种新损失函数联合插值网络和分割网络进行整个框架的优化训练。实验结果表明，相比于其他现有深度学习方法，本文方法取得了更好的肝胆管分割性能，更有利于肝胆管三维重建。

Abstract

Hepatobiliary stone disease is a common liver disease and has become the main cause of death from non-neoplastic biliary diseases in China

and it is important to achieve interpolated segmentation reconstruction between slices of hepatobiliary ducts. In this research

an end-to-end framework for super-resolution abdominal CT image processing is proposed based on deep correlation mechanism. The framework cascades an inter-slice interpolation network and a segmentation network

in which the ConvLSTM is introduced to enhance the extraction of high-dimensional feature information of hepatobiliary ducts between slices. A novel loss is designed by combining the loss of the interpolation network and the loss of the segmentation network. Experimental results show that the proposed framework is superior to the existing deep learning methods for the segmentation of hepatobiliary ducts

which is beneficial for the 3D reconstruction of hepatobiliary ducts.

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references

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