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Purpose <p>The objective of this study was to develop an automatic two-stage diagnostic system for detecting and classifying cervical spine fractures using 3D segmentation and 2.5D classification approaches on CT images. The first stage aimed to achieve accurate 3D segmentation of cervical spine structures, while the second stage focused on classifying fractures at both the individual cervical vertebra level and the overall patient level.</p> Methods <p>The study utilized a dataset of 87 cases with pixel-level semantic segmentation annotations and 2019 CT scans. A 3D segmentation model was employed for automatic segmentation, followed by a fracture classification system using 2.5D deep learning models. Data augmentation and preprocessing techniques were applied to enhance model performance. The models were trained and evaluated using 5-fold cross-validation, with performance metrics including Dice coefficient, Recall, Specificity, F1 Score, AUC, and Accuracy.</p> Results <p>In the first stage, the 3D segmentation model achieved a Dice coefficient of 0.93 on the test set, indicating high segmentation accuracy for cervical spine structures. In the second stage, the classification models achieved accuracies ranging from 0.8980 to 0.9812 at both the C1–C7 vertebra level and the overall patient level. However, there were noticeable variations in sensitivity and positive predictive value across different vertebral levels, with relatively lower sensitivity observed at C3 and C5–C6. Overall, the proposed two-stage system demonstrated good performance in fracture detection and localization and outperformed previously reported methods on several evaluation metrics.</p> Conclusion <p>This study developed a two-stage automatic diagnostic system based on 3D segmentation and 2.5D classification for the detection and localization of cervical spine fractures. The system achieved improved diagnostic accuracy and efficiency in our study cohort and showed certain potential for clinical application. Nevertheless, we observed substantial variability in sensitivity and precision across different vertebrae, and the detection performance for some levels (such as C3 and C5–C6) remained suboptimal. In addition, the absence of external validation limits the direct generalizability of the model to clinical practice. Future work should focus on further optimization and validation of the system in larger, more diverse, and multicenter cohorts to enhance its generalizability and clinical utility.</p>

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A two-stage deep learning system for cervical spine fracture diagnosis: integrating 3D segmentation and 2.5D classification on CT images

  • 期刊论文

  • 发表日期：

    2026年3月30日

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  • Renyi Lu,
  • Yuying Feng,
  • Ruozhou Wang,
  • Ting Song

摘要

Purpose

The objective of this study was to develop an automatic two-stage diagnostic system for detecting and classifying cervical spine fractures using 3D segmentation and 2.5D classification approaches on CT images. The first stage aimed to achieve accurate 3D segmentation of cervical spine structures, while the second stage focused on classifying fractures at both the individual cervical vertebra level and the overall patient level.

Methods

The study utilized a dataset of 87 cases with pixel-level semantic segmentation annotations and 2019 CT scans. A 3D segmentation model was employed for automatic segmentation, followed by a fracture classification system using 2.5D deep learning models. Data augmentation and preprocessing techniques were applied to enhance model performance. The models were trained and evaluated using 5-fold cross-validation, with performance metrics including Dice coefficient, Recall, Specificity, F1 Score, AUC, and Accuracy.

Results

In the first stage, the 3D segmentation model achieved a Dice coefficient of 0.93 on the test set, indicating high segmentation accuracy for cervical spine structures. In the second stage, the classification models achieved accuracies ranging from 0.8980 to 0.9812 at both the C1–C7 vertebra level and the overall patient level. However, there were noticeable variations in sensitivity and positive predictive value across different vertebral levels, with relatively lower sensitivity observed at C3 and C5–C6. Overall, the proposed two-stage system demonstrated good performance in fracture detection and localization and outperformed previously reported methods on several evaluation metrics.

Conclusion

This study developed a two-stage automatic diagnostic system based on 3D segmentation and 2.5D classification for the detection and localization of cervical spine fractures. The system achieved improved diagnostic accuracy and efficiency in our study cohort and showed certain potential for clinical application. Nevertheless, we observed substantial variability in sensitivity and precision across different vertebrae, and the detection performance for some levels (such as C3 and C5–C6) remained suboptimal. In addition, the absence of external validation limits the direct generalizability of the model to clinical practice. Future work should focus on further optimization and validation of the system in larger, more diverse, and multicenter cohorts to enhance its generalizability and clinical utility.