Automatic segmentation of identified vertebral bones from CT images using CA-SegResNet
10.3969/j.issn.1005-202X.2024.11.005
- VernacularTitle:基于CA-SegResNet的CT图像标识椎骨自动分割
- Author:
Zhongqi ZHU
1
;
Xiaolong GAO
;
Yinghao LI
;
Guang YANG
;
Liguo HAO
;
Hongzhi WANG
Author Information
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
- Keywords:
deep learning;
computed tomography;
vertebral segmentation;
segmentation network;
coordinate attention
- From:
Chinese Journal of Medical Physics
2024;41(11):1349-1356
- CountryChina
- Language:Chinese
-
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 0.0463,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.
