2024/09/28 by Maryam Tavakol Elahi, Elahi, Maryam Tavakol
Engineering · Neuroscience · #Brain Tumor Detection and Classification #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #I.4.6 #Image and Video Processing (eess.IV) #Medical Imaging and Analysis #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2409.19354
openalex publication_date 2024/09/28 · openalex created_date 2024/10/28 · openalex updated_date 2026/07/28
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.