Automated segmentation of the ulnar nerve in mri using deep learning techniques

Author

Akhil Nagulapalli, New Jersey Institute of TechnologyFollow

Document Type

Thesis

Date of Award

12-31-2024

Degree Name

Master of Science in Biomedical Engineering - (M.S.)

Department

Biomedical Engineering

First Advisor

Chang Yaramothu

Second Advisor

Ek Tsoon Tan

Third Advisor

Elisa Kallioniemi

Fourth Advisor

Xuan Liu

Abstract

Cubital Tunnel Syndrome (CuTS), a condition caused by compression of the ulnar nerve, results in numbness, tingling, pain, and even muscle atrophy, affecting fine motor skills and diminishing patient quality of life. Accurate diagnosis of CuTS is challenging, as current diagnostic methods—including clinical exams, nerve conduction studies, and unaided MRI—often lack the precision to reliably identify the nerve and detect compression in its early stages. Deep learning-based segmentation offers a promising solution, enabling precise and automated identification of nerve structures in MRI images, which could significantly improve diagnostic accuracy and support timely intervention.

A novel deep learning model for segmenting the ulnar nerve in MRI images, supporting accurate and early diagnosis of CuTS, is proposed. The model, a multi-scale U-Net with spatial attention and dynamic loss weighting, is designed to overcome segmentation challenges specific to ulnar nerve anatomy by capturing both global context and fine details. Experimental results show that the model reliably identifies the ulnar nerve in test images, demonstrating its potential as a diagnostic tool for improving the accuracy and efficiency of CuTS detection.

Recommended Citation

Nagulapalli, Akhil, "Automated segmentation of the ulnar nerve in mri using deep learning techniques" (2024). Theses. 2948.
https://digitalcommons.njit.edu/theses/2948