M-SCAN: A Multistage Framework for Lumbar Spinal Canal Stenosis Grading Using Multi-View Cross Attention

• URL: http://arxiv.org/abs/2503.01634v1
• Date: Mon, 03 Mar 2025 15:10:40 GMT
• Title: M-SCAN: A Multistage Framework for Lumbar Spinal Canal Stenosis Grading Using Multi-View Cross Attention
• Abstract summary: This paper introduces a novel and efficient deep-learning framework that fully automates the grading of lumbar spinal canal stenosis.<n>We demonstrate state-of-the-art performance in grading spinal canal stenosis on a dataset of 1,975 unique studies.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The increasing prevalence of lumbar spinal canal stenosis has resulted in a surge of MRI (Magnetic Resonance Imaging), leading to labor-intensive interpretation and significant inter-reader variability, even among expert radiologists. This paper introduces a novel and efficient deep-learning framework that fully automates the grading of lumbar spinal canal stenosis. We demonstrate state-of-the-art performance in grading spinal canal stenosis on a dataset of 1,975 unique studies, each containing three distinct types of 3D cross-sectional spine images: Axial T2, Sagittal T1, and Sagittal T2/STIR. Employing a distinctive training strategy, our proposed multistage approach effectively integrates sagittal and axial images. This strategy employs a multi-view model with a sequence-based architecture, optimizing feature extraction and cross-view alignment to achieve an AUROC (Area Under the Receiver Operating Characteristic Curve) of 0.971 in spinal canal stenosis grading surpassing other state-of-the-art methods.

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