Title: Segmentation of the human spinal cord using U-Net architecture

Authors: S. Kumarganesh; Muzammil Hussain; H. Shaheen; S. Anthoniraj; M. Somaskandan; C. Sivakumaran

Addresses: ECE, Knowledge Institute of Technology, Salem, Tamilnadu, India ' Department of ISE, MVJ College of Engineering, Bangalore, Karnataka, India ' Department of AIML, Hindusthan College of Engineering and Technology, Coimbatore, Tamilnadu, India ' Department of ISE, MVJ College of Engineering, Bangalore, Karnataka, India ' Department of IT, Panimalar Engineering College, Chennai, Tamilnadu, India ' Photon Technologies, Chennai, Tamilnadu, India

Abstract: When attempting to assess spinal cord atrophy caused by a variety of disorders, the first step that must be taken is to segment the spinal cord contour. A tumour of the spinal cord is an abnormal development of cells that may occur anywhere in or around the spinal cord. The process of locating tumours in the spinal cord is a very important one. It is difficult to identify the tumour with MRI due to the irregular form of the spinal cord. The model begins by locating the spinal cord, after which it creates the bounding box coordinates. Our technique is validated using four separate clinical datasets. The results of the experiments that used a unique segmentation strategy that was dependent on MRI images reveal that the algorithm that was presented for the system delivers a higher level of accuracy when compared to the other algorithms that are already in use.

Keywords: spinal cord segmentation; bounding box; U-Net architecture; deep learning.

DOI: 10.1504/IJMEI.2025.145040

International Journal of Medical Engineering and Informatics, 2025 Vol.17 No.2, pp.116 - 127

Received: 21 May 2022
Accepted: 22 Jul 2022
Published online: 18 Mar 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article