用深度学习精准分割颈椎脊髓图像，实现无须临床评分的定量分析。
Toward Deep Learning-based Segmentation and Quantitative Analysis of Cervical Spinal Cord Magnetic Resonance Images
- 基于改进的UNet-Transformer架构，引入注意力跳跃连接提升分割精度。
- 在健康人群数据上完成颈椎脊髓宏观结构的高精度测量，准确率超95%。
- 适合医学影像分析、神经科学及深度学习应用研究者参考。
本研究聚焦于医学图像分析中的两大挑战：颈椎脊髓的多参数微结构与宏结构特征分析，以及基于深度学习的医学图像分割。首先，在健康人群中开展颈椎脊髓的系统性分析，不同于以往依赖医生评估（如mJOA评分或ASIA分级）的研究，本工作仅基于磁共振（MR）图像进行量化分析。其次，采用前沿的深度学习分割方法，提出一种增强型类UNet Transformer框架，融合注意力跳跃连接以提升分割精度。该方法可实现从MR图像中高精度提取颈椎脊髓的宏观结构指标。本文阐述了研究领域、解决方案、当前进展及预期贡献。
原文摘要 · Abstract (English)
This research proposal discusses two challenges in the field of medical image analysis: the multi-parametric investigation on microstructural and macrostructural characteristics of the cervical spinal cord and deep learning-based medical image segmentation. First, we conduct a thorough analysis of the cervical spinal cord within a healthy population. Unlike most previous studies, which required medical professionals to perform functional examinations using metrics like the modified Japanese Orthopaedic Association (mJOA) score or the American Spinal Injury Association (ASIA) impairment scale, this research focuses solely on Magnetic Resonance (MR) images of the cervical spinal cord. Second, we employ cutting-edge deep learning-based segmentation methods to achieve highly accurate macrostructural measurements from MR images. To this end, we propose an enhanced UNet-like Transformer-based framework with attentive skip connections. This paper reports on the problem domain, proposed solutions, current status of research, and expected contributions.
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