Identifying Pediatric Crohn’s Disease Using Deep Learning to Classify Magnetic Resonance Enterography (MRE) Images

Abstract

Crohn’s Disease (CD) diagnosis is a constant challenge for clinicians. Even with extensive magnetic resonance enterography (MRE) scans, identifying tissue damaged by CD can still be difficult, even for experts. Deep learning approaches for medical applications have recently gained traction as tools to complement radiologist consultation. Computer aided diagnosis can potentially save time and labor resources spent on routine manual diagnosis. For imaging of the gastrointestinal tract, these cutting-edge techniques could help distinguish subtle structures indicative of Crohn’s Disease (CD) that are not visible to the human eye. In this paper, we explore existing segmentation and neural network approaches more traditionally used for non-medical imaging and compare their diagnostic potential for identifying CD from MRE images.