Objective
Vertebral segmentation in computed tomography (CT) images remains an essential issue in medical image analysis, stemming from the variability in vertebral shapes, high complex deformations, and the inherent ambiguity in CT scans. The purpose of this study was to develop advanced methods to effectively address this challenging task.
Methods
We proposed an attention-driven asymmetric convolution deep learning (AACDL) framework for vertebral segmentation. Specifically, our approach involved constructing a novel asymmetric convolutional U-shaped deep learning architecture to enhance the feature extraction capabilities by increasing its depth for capturing richer spatial details. Further, we constructed a pyramid global context module that integrates global context information through pyramid pooling to boost segmentation accuracy particularly in smaller anatomical regions. Sequential channel and spatial attention mechanisms were also implemented within the network to enable it to automatically concentrate on learning the most salient features and regions across different dimensions.
Results
The performance precision of our network was rigorously assessed using a suite of four benchmark metrics: the dice coefficient, mean intersection over union (mIoU), precision rate, and F1-score. When compared against the ground truth, our model delivered outstanding scores, attaining a dice coefficient of 82.79%, an mIoU of 90.72%, a precision rate of 90.19%, and an F1-score of 90.09%, each reflecting the commendable accuracy and reliability of our network's segmentation output.
Conclusion
The proposed AACDL method might successfully realize accurate segmentation of vertebral CT images, thereby demonstrating significant potential for clinical applications with its robust performance metrics. Its ability to handle the complexities associated with vertebral segmentation may pave the way for enhanced diagnostic and treatment planning processes in healthcare settings.
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