Learning from the unseen: synthetic data for scoliosis AI segmentation.

Abstract

Purpose: Severe scoliosis remains challenging for 3D vertebral segmentation because annotated pathological computed tomography (CT) data are scarce.

Methods: We synthesize scoliosis-like CT volumes by jointly warping public healthy spine scans and labels and then train nnU-Net CNN and PRIMUS models under matched compute. We evaluate on in-house out-of-distribution (OOD) adolescent scoliosis CTs and the public Spine1K dataset.

Results: On 18 in-house adolescent scoliosis CTs, the best CNN setting increased mean Dice from 0.765 to 0.842 and vertebra success rate from 73.2 to 81.6%. In 55 surgeon-defined apical labels, mean Dice increased from 0.720 to 0.854. Spine1K performance remained high when synthetic data supplemented real scans; synthetic-only training was less stable.

Conclusion: Pathology-informed synthetic data improve robustness to severe scoliosis without new annotations or architecture changes. Code and interactive visualizations are available at: https://chepchikvaleri.github.io/synth-data-s/.