Evidence map›Paper›PMID 42864529›Full record

ArticleInternational journal of computer assisted radiology and surgery2026

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

Volodimir Buchakchiyskiy, Sidaty El Hadramy, Norbert Zentai, Michael Wehrli, Zdzislaw Krol, Giada Cescato, Maria Licci, Daniel Studer, Carol C Hasler, Philippe C Cattin

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Article in International journal of computer assisted radiology and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Volodimir BuchakchiyskiyDepartment of Biomedical Engineering, University of Basel, Allschwil, Switzerland. volodymyr.buchakchiiskyi@unibas.ch.ORCID http://orcid.org/0009-0000-6865-9344
Sidaty El HadramyDepartment of Biomedical Engineering, University of Basel, Allschwil, Switzerland.
Norbert ZentaiDepartment of Biomedical Engineering, University of Basel, Allschwil, Switzerland.
Michael WehrliDepartment of Biomedical Engineering, University of Basel, Allschwil, Switzerland.
Zdzislaw KrolDepartment of Orthopedics, University Children's Hospital Basel, Basel, Switzerland.
Giada CescatoFaculty of Medicine, University of Basel, Basel, Switzerland.
Maria LicciDepartment of Orthopedics, University Children's Hospital Basel, Basel, Switzerland.
Daniel StuderDepartment of Orthopedics, University Children's Hospital Basel, Basel, Switzerland.
Carol C HaslerDepartment of Orthopedics, University Children's Hospital Basel, Basel, Switzerland.
Philippe C CattinDepartment of Biomedical Engineering, University of Basel, Allschwil, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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

methodsWe 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.

resultsOn 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.

conclusionPathology-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/.

Indexed as

Artificial dataData augmentationScoliosisSegmentation

Identifiers

PMID42864529

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.