Volume 14, Number 4

Segmentation of the Gastrointestinal Tract MRI Using Deep Learning

  Authors

J. Roy and A. Abdel-Dayem, Laurentian University, Canada

  Abstract

This paper proposes a deep learning-based model to segment gastrointestinal tract (GI) magnetic resonance images (MRI). The application of this model will be useful in potentially accelerating treatment times and possibly improve the quality of the treatments for the patients who must undergo radiation treatments in cancer centers. The proposed model employs the U-net architecture, which provides outstanding overall performance in medical image segmentation tasks. The model that was developed through this project has a score of 81.86% using a combination of the dice coefficient and the Hausdorff distance measures, rendering it highly accurate in segmenting and contouring organs in the gastrointestinal system.

  Keywords

Machine Learning, Cancer Diagnosis, Supervised Learning, Computer Vision, Semantic Segmentation.