[1]朱中旗,高晓隆,李英豪,等.基于CA-SegResNet的CT图像标识椎骨自动分割[J].中国医学物理学杂志,2024,41(11):1349-1356.[doi:DOI:10.3969/j.issn.1005-202X.2024.11.005]
　ZHU Zhongqi,GAO Xiaolong,LI Yinghao,et al.Automatic segmentation of identified vertebral bones from CT images using CA-SegResNet[J].Chinese Journal of Medical Physics,2024,41(11):1349-1356.[doi:DOI:10.3969/j.issn.1005-202X.2024.11.005]
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基于CA-SegResNet的CT图像标识椎骨自动分割()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
41卷
期数:
2024年第11期
页码:
1349-1356
栏目:
医学影像物理
出版日期:
2024-11-26

文章信息/Info

Title:
Automatic segmentation of identified vertebral bones from CT images using CA-SegResNet
文章编号:
1005-202X（2024）11-1349-08
作者:
朱中旗1; 高晓隆2; 李英豪1; 杨光1; 郝利国3; 汪红志1
1.华东师范大学物理与电子科学学院上海市磁共振重点实验室， 上海 200062； 2.富锦市中医医院影像科， 黑龙江 富锦 156100； 3.齐齐哈尔医学院医学技术学院分子影像研究室， 黑龙江 齐齐哈尔 161006
Author(s):
ZHU Zhongqi1;  GAO Xiaolong2;  LI Yinghao1;  YANG Guang1;  HAO Liguo3;  WANG Hongzhi1
1. Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, China 2. Department of Imaging, Fujin Chinese Medicine Hospital, Fujin 156100, China 3. Laboratory of Molecular Imaging, School of Medical Technology, Qiqihar Medical University, Qiqihar 161006, China
关键词:
深度学习; 计算机断层扫描; 椎骨分割; 分割网络; 坐标注意力
Keywords:
Keywords: deep learning computed tomography vertebral segmentation segmentation network coordinate attention
分类号:
R318；TP391.41
DOI:
DOI:10.3969/j.issn.1005-202X.2024.11.005
文献标志码:
A
摘要:
针对脊椎计算机断层扫描（CT）图像标识椎骨分割问题，提出一种融合三维坐标注意力机制的三维医学图像分割网络CA-SegResNet。该网络通过深度残差卷积神经网络提取图像特征，将编码器每层输出的特征图与解码器每层的输入相融合，然后引入三维坐标注意力模块捕获通道间关系以及方向和位置信息，建立起不同空间方向的长距离依赖关系，实现标识椎骨的精准分割。在对105个病例的颈椎标识椎骨（第7节颈椎）和胸椎标识椎骨（第12节胸椎）的分割任务中，CA-SegResNet在测试集上的分割平均Dice系数（DSC）分别为0.934 5和0.918 9，平均豪斯多夫距离（HD）为7和8 mm。与U-Net相比，平均DSC分别提高0.014 5和0.046 3，平均HD分别缩小176和388 mm。实验结果表明，该算法能够对CT图像标识脊椎进行精准分割。
Abstract:
Abstract: A three-dimensional (3D) medical image segmentation network (CA-SegResNet) which incorporates a 3D coordinate attention mechanism is proposed to address the issue of segmenting identified vertebral bones from spinal computed tomography (CT) images. The network extracts image features through a deep residual convolutional neural network and fuses the feature maps from each encoder layer with the input of the corresponding decoder layer. Subsequently, a 3D coordinate attention module is introduced to capture inter-channel relationships as well as directional and positional information, establishing long-range dependencies across different spatial directions, thereby enabling precise segmentation of the identified vertebral bones. For the segmentation tasks involving the identified cervical vertebra (the 7th cervical vertebra) and the identified thoracic vertebra (the 12th thoracic vertebra) across 105 cases, CA-SegResNet achieves average Dice similarity coefficients (DSC) of 0.934 5 and 0.918 9 on the test set, with average Hausdorff distances (HD) of 7 and 8 mm. Compared with U-Net results, the average DSC is improved by 0.014 5 and 0.046 3, while average HD is reduced by 176 and 388 mm. The results demonstrate that the network can realize the precise segmentation of identified vertebral bones from CT images.

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备注/Memo

备注/Memo:
【收稿日期】2024-05-07 【基金项目】国家自然科学基金（61731009） 【作者简介】朱中旗，硕士研究生，研究方向：人工智能在医学影像中的应用，E-mail: arcmosin@163.com；高晓隆，硕士，主治医师，研究方向：医学影像诊断、医学影像AI技术，E-mail: 185977790@qq.com（朱中旗和高晓隆为共同第一作者） 【通信作者】郝利国，硕士，教授，研究方向：影像医学与核医学，E-mail: haoliguo@qmu.edu.cn；汪红志，博士，高级工程师，CCF会员，研究方向：医学影像设备与技术、医学影像人工智能技术，E-mail: hzwang@phy.ecnu.edu.cn
更新日期/Last Update: 2024-11-26