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Toward Deep Learning-based Segmentation and Quantitative Analysis of Cervical Spinal Cord Magnetic Resonance Images

Maryam Tavakol Elahi

TL;DR

The paper tackles objective, quantitative analysis of microstructural and macrostructural characteristics of the healthy cervical spinal cord using multi-parametric MRI, and advances segmentation quality with a Transformer-based, attention-enhanced UNet variant. It proposes SAttisUNet, a Swin Transformer–based encoder with an attentive skip-connection module to improve high-resolution per-vertebral segmentation and macrostructural measurement accuracy. The study details a multiparametric imaging protocol across three 3T MR machines, deriving $CSA$, $SAC$, $SAC/CSA$, and diffusion metrics ($FA$, $MD$, $RD$) to analyze micro–macro relationships and the influence of gender and scanner type in healthy subjects. It also outlines ablation studies and future extensions to diffusion-weighted imaging and multimodal classification, aiming to deliver robust, automated tools for spinal cord analysis with potential biomarkers for neurological conditions.

Abstract

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.

Toward Deep Learning-based Segmentation and Quantitative Analysis of Cervical Spinal Cord Magnetic Resonance Images

TL;DR

The paper tackles objective, quantitative analysis of microstructural and macrostructural characteristics of the healthy cervical spinal cord using multi-parametric MRI, and advances segmentation quality with a Transformer-based, attention-enhanced UNet variant. It proposes SAttisUNet, a Swin Transformer–based encoder with an attentive skip-connection module to improve high-resolution per-vertebral segmentation and macrostructural measurement accuracy. The study details a multiparametric imaging protocol across three 3T MR machines, deriving , , , and diffusion metrics (, , ) to analyze micro–macro relationships and the influence of gender and scanner type in healthy subjects. It also outlines ablation studies and future extensions to diffusion-weighted imaging and multimodal classification, aiming to deliver robust, automated tools for spinal cord analysis with potential biomarkers for neurological conditions.

Abstract

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.
Paper Structure (9 sections, 3 figures)

This paper contains 9 sections, 3 figures.

Table of Contents

  1. Introduction and Motivation
  2. Related Work
  3. Cervical spinal cord disease analysis
  4. Deep learning-based medical image analysis
  5. Proposed Solutions
  6. Insights into Cervical Spinal Cord and Imaging Modalities
  7. Deep Learning-based Methodology for Medical Image Segmentation
  8. Expected Contributions
  9. Conclusion

Figures (3)

  • Figure 1: Left: cervical spinal cord and cerebrospinal fluid (CSF) segmentation, Middle: cross-sectional view of cord (CSA) and SAC segmentation, Right: per-vertebral level segmentation.
  • Figure 2: The architecture of the proposed SAttisUNet for enhanced medical image segmentation.
  • Figure 3: The qualitative segmentation results of SAttisUNet on the cervical spinal cord dataset from the sagittal view.