TotalSpineSeg: automated whole-spine vertebral segmentation with high robustness to labeling errors

Objectives: To develop TotalSpineSeg, a robust tool for vertebrae and intervertebral disc instance segmentation, along with spinal cord and spinal canal segmentation, capable of processing any 3D medical image regardless of contrast, resolution, or field of view, while overcoming fusion and fragmentation errors common in standard segmentation. Materials and methods: A two-stage cascaded deep learning model was trained on 1404 MRIs. To explicitly prevent errors where adjacent vertebrae merge or a single vertebra splits, the architecture employs a novel “odd/even” representation. The second stage is conditioned on an odd-intervertebral disc mask from the first stage to assign distinct semantic classes to alternating vertebrae, enforcing accurate separation between neighboring vertebrae. Finally, a landmark-based logic uses key anatomical points (C2–C3, C7–T1, T12–L1, and L5–S) to anchor sequential labeling, while a localizer-based strategy ensures accurate identification even in truncated, short field-of-view scans. Results: Evaluation across diverse contrasts and pathologies yielded a vertebral identification rate of 96.22% on localizer MRI and generalized to CT scans with a 92.87% identification rate. For intervertebral disc instance labeling, where vertebral separation is not mandatory, both model stages attained 99% labeling accuracy with a mean positional L2 error of 1.55 mm, surpassing existing state-of-the-art methods. Conclusion: TotalSpineSeg successfully generalizes to diverse 3D medical images, overcoming variations in contrast, resolution, and field of view. The proposed topological constraints effectively eliminate vertebral fusion and fragmentation errors, delivering a state-of-the-art identification rate for both vertebrae and intervertebral discs across MRI and CT modalities.