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

TL;DR

This paper proposes a deep learning framework for segmentation and analysis of cervical spinal cord MRI images, aiming to improve microstructural and macrostructural assessment without relying on functional examinations.

Contribution

It introduces a novel Transformer-based UNet-like model with attentive skip connections for accurate segmentation of spinal cord MR images.

Findings

Analysis of healthy cervical spinal cords from MR images

Development of a new deep learning segmentation model

Expected improvements in measurement accuracy

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.