用多视角注意力模型自动分级腰椎管狭窄，准确率超97%
M-SCAN: A Multistage Framework for Lumbar Spinal Canal Stenosis Grading Using Multi-View Cross Attention
- 分阶段融合矢状位与轴向MRI图像，通过序列化架构对齐多视图特征
- 在1975例数据上实现0.971的AUROC，优于现有方法
- 适合放射科医生辅助诊断，尤其应对阅片压力大的场景
腰椎管狭窄发病率上升导致MRI检查量激增，人工解读耗时且存在显著阅片者差异，即使专家间亦然。本文提出一种新型高效深度学习框架，可完全自动化完成腰椎管狭窄分级。在包含1,975个独立病例的数据集上验证，每例含三种3D脊柱横断面图像：轴向T2、矢状位T1、矢状位T2/STIR。采用独特训练策略，所提多阶段方法有效融合矢状与轴向图像。该方法基于序列化架构的多视图模型，优化特征提取与跨视图对齐，在脊柱管狭窄分级任务中达到0.971的AUROC（受试者工作特征曲线下面积），超越其他先进方法。
原文摘要 · Abstract (English)
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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