# SCIsegV2: A Universal Tool for Segmentation of Intramedullary Lesions in Spinal Cord Injury

arXiv: https://arxiv.org/abs/2407.17265 · HTML: https://mlsanity.com/paper/2407.17265/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 24 Jul 2024.

## Builds on (most cited first, 6 of 6)

- [U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation](https://mlsanity.com/paper/2401.04722/) (2024)
- [nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation](https://mlsanity.com/paper/2404.09556/) (2024)
- [SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining](https://mlsanity.com/paper/2107.09559/) (2021)
- [Metrics reloaded: Recommendations for image analysis validation](https://mlsanity.com/paper/2206.01653/) (2022)
- [MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation](https://mlsanity.com/paper/2303.09975/) (2023)
- [Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks](https://mlsanity.com/paper/1805.06349/) (2018)

## Built on by (most cited first, 1 of 1)

- [Monitoring morphometric drift in lifelong learning segmentation of the spinal cord](https://mlsanity.com/paper/2505.01364/) (2025)

## Abstract (arXiv)

Spinal cord injury (SCI) is a devastating incidence leading to permanent paralysis and loss of sensory-motor functions potentially resulting in the formation of lesions within the spinal cord. Imaging biomarkers obtained from magnetic resonance imaging (MRI) scans can predict the functional recovery of individuals with SCI and help choose the optimal treatment strategy. Currently, most studies employ manual quantification of these MRI-derived biomarkers, which is a subjective and tedious task. In this work, we propose (i) a universal tool for the automatic segmentation of intramedullary SCI lesions, dubbed textttSCIsegV2, and (ii) a method to automatically compute the width of the tissue bridges from the segmented lesion. Tissue bridges represent the spared spinal tissue adjacent to the lesion, which is associated with functional recovery in SCI patients. The tool was trained and validated on a heterogeneous dataset from 7 sites comprising patients from different SCI phases (acute, sub-acute, and chronic) and etiologies (traumatic SCI, ischemic SCI, and degenerative cervical myelopathy). Tissue bridges quantified automatically did not significantly differ from those computed manually, suggesting that the proposed automatic tool can be used to derive relevant MRI biomarkers. textttSCIsegV2 and the automatic tissue bridges computation are open-source and available in Spinal Cord Toolbox (v6.4 and above) via the textttsctdeepseg -task segsclesiont2wsci and textttsctanalyzelesion functions, respectively.