Evaluating Multi-Channel Input of Sagittal T2-weighted and STIR MRI for Automated Segmentation of Spinal Cord MS Lesions

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Identifying multiple sclerosis lesions in spinal cord MRI is important but challenging, and automated methods have been proposed to reduce inter-rater variability. Although clinicians typically use multiple MR sequences to identify lesions for a given patient, most automated methods use only a single sequence as input. We investigated combining sagittal T2-weighted and STIR sequences using an early fusion U-Net for lesion segmentation, assessing the impact of resampling techniques and U-Net architectures (3D vs. hybrid 2D/3D). Results showed no overall improvement from adding STIR (p=0.17), though this varied among subjects. Furthermore, T2-only models trained on larger datasets outperformed T2+STIR models trained on subsets, indicating that a larger single-sequence dataset is preferable to a smaller multi-sequence one.

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