Ca-Segresnet: A Context-Aware Segmentation Residual Network for Automatic Segmentation of Identified Vertebral Bones from Ct Images

Abstract

Accurate segmentation of vertebral bones in computer tomography (CT) spine images is critical for the diagnosis and treatment of spinal diseases. In this paper, we propose an improved network called CA-SegResNet, which utilizes residual convolutional neural networks to extract image features. CA-SegResNet combines the feature maps outputted by each layer of the encoder with the inputs of each layer of the decoder. Additionally, it introduces a Three-Dimensional Coordinate Attention module to capture inter-channel relationships, direction, and position information, allowing for the establishment of long-range dependencies in different spatial directions for the localization and segmentation of identified vertebral bones. In the segmentation task of cervical vertebrae (C7) and thoracic vertebrae (T12) for each of the 105 cases, CA-SegResNet achieved a localization accuracy rate of 100% for identified vertebrae on the test dataset. The average Dice coefficients for the segmentation results were 93.45% and 91.89%, with average Hausdorff distances of 7mm and 8mm, respectively. Compared to UNet, CA-SegResNet shows an increase of 0.0145 and 0.0463 in average Dice coefficients, and a decrease in average Hausdorff distances of 176mm and 388mm for C7 and T12, respectively. The results suggest that the proposed model can accurately segment vertebral spines on CT images and identify them based on their localization. Moreover, the study can potentially save up to 90% of the workload of manual annotation compared with segmenting the entire spine.

Note:
Funding declaration: Financial support from Shanghai Domestic Science and Technology Cooperation Project (21015801000) is gratefully acknowledged.

Conflict of Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethical Approval: This study is a retrospective study and has been exempted by the local institutional ethics review board. Before the scans, patients were informed about the scanning procedure, obtained their consent, and signed informed consent forms.

Keywords: 【Keywords】Spine segmentation, CT images, CA-SegResNet, Deep learning, Coordinate Attention