International journal of computer assisted radiology and surgery · 2026
Learning from the unseen: synthetic data for scoliosis AI segmentation.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
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Buchakchiyskiy Volodimir, El Hadramy Sidaty, Zentai Norbert, Wehrli Michael, Krol Zdzislaw, Cescato Giada, Licci Maria, Studer Daniel, Hasler Carol C, Cattin Philippe C
Аннотация
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/.
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/.