SCIseg: Automatic Segmentation of T2-weighted Hyperintense Lesions in Spinal Cord Injury

Background: Quantitative MRI biomarkers in spinal cord injury (SCI) can help understand the extent of the focal injury. However, due to the lack of automatic segmentation methods, these biomarkers are derived manually, which is a time-consuming process prone to intra- and inter-rater variability, thus limiting large multi-site studies and translation to clinical workflows. Purpose: To develop a deep learning tool for the automatic segmentation of T2-weighted hyperintense lesions and the spinal cord in SCI patients. Material and Methods: This retrospective study included a cohort of SCI patients from three sites enrolled between July 2002 and February 2023 who underwent clinical MRI examination. A deep learning model, SCIseg, was trained on T2-weighted images with heterogeneous image resolutions (isotropic, anisotropic), and orientations (axial, sagittal) acquired using scanners from different manufacturers (Siemens, Philips, GE) and different field strengths (1T, 1.5T, 3T) for the automatic segmentation of SCI lesions and the spinal cord. The proposed method was visually and quantitatively compared with other open-source baseline methods. Quantitative biomarkers (lesion volume, lesion length, and maximal axial damage ratio) computed from manual ground-truth lesion masks and automatic SCIseg predictions were correlated with clinical scores (pinprick, light touch, and lower extremity motor scores). A between-group comparison was performed using the Wilcoxon signed-rank test. Results: MRI data from 191 SCI patients (mean age, 48.1 years ± 17.9 [SD]; 142 males) were used for training. Compared to existing methods, SCIseg achieved the best segmentation performance for both the cord and lesions and generalized well to both traumatic and non-traumatic SCI patients. SCIseg is open-source and accessible through the Spinal Cord Toolbox. Conclusion: Automatic segmentation of intramedullary lesions commonly seen in traumatic SCI replaces the tedious manual annotation process and enables the extraction of relevant lesion morphometrics in large cohorts. The proposed model generalizes across lesion etiologies (traumatic, ischemic), scanner manufacturers and heterogeneous image resolutions. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was funded by the Canada Research Chair in Quantitative Magnetic Resonance Imaging [CRC–2020–00179], the Canadian Institute of Health Research [PJT–190258], the Canada Foundation for Innovation [32454, 34824], the Fonds de Recherche du Quebec – Sante [322736, 324636], the Natural Sciences and Engineering Research Council of Canada [RGPIN–2019–07244], the Canada First Research Excellence Fund (IVADO and TransMedTech), the Courtois NeuroMod project, the Quebec BioImaging Network [5886, 35450], INSPIRED (Spinal Research, UK; Wings for Life, Austria; Craig H. Neilsen Foundation, USA), Mila – Tech Transfer Funding Program, the Association Francaise contre les Myopathies (AFM), the Institut pour la Recherche sur la Moelle epiniere et Encephale (IRME), and the Ministry of Health of the Czech Republic, grant nr. NU22–04–00024, the National Institutes of Health Eunice Kennedy Shriver National Institute of Child Health and Development (R03HD094577). ACS is supported by the National Institutes of Health – K01HD106928 and R01NS128478 and the Boettcher Foundation Webb-Waring Biomedical Research Program. KAW is supported by the National Institutes of Health – K23NS104211, L30NS108301, R01NS128478. JV received funding from the European Union Horizon Europe research and innovation programme under the Marie Sktodowska-Curie grant agreement No 101107932. NKE is supported by the Fonds de Recherche du Quebec Nature and Technologie (FRQNT) Doctoral Training Scholarship and in part by the FRQNT Strategic Clusters Program (2020–RS4–265502 – Centre UNIQUE – Union Neurosciences – Artificial Intelligence – Quebec and in part, by funding from the Canada First Research Excellence Fund through the TransMedTech Institute. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The local ethics committee of the Balgrist University Hospital, Zurich, gave ethical approval for this work. The local ethics committee of the University of Colorado School of Medicine and Craig Hospital, Englewood, Colorado, gave ethical approval for this work. The local ethics committee of the Pitie-Salpetriere Hospital, Paris, gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors [https://github.com/ivadomed/model\_seg\_sci][1] [1]: https://github.com/ivadomed/model_seg_sci