AutoComb: Automated Comb Sign Detector for 3D CTE Scans
Journal:
arXiv
Published Date:
Feb 28, 2025
Abstract
Comb Sign is an important imaging biomarker to detect multiple
gastrointestinal diseases. It shows up as increased blood flow along the
intestinal wall indicating potential abnormality, which helps doctors diagnose
inflammatory conditions. Despite its clinical significance, current detection
methods are manual, time-intensive, and prone to subjective interpretation due
to the need for multi-planar image-orientation. To the best of our knowledge,
we are the first to propose a fully automated technique for the detection of
Comb Sign from CTE scans. Our novel approach is based on developing a
probabilistic map that shows areas of pathological hypervascularity by
identifying fine vascular bifurcations and wall enhancement via processing
through stepwise algorithmic modules. These modules include utilising deep
learning segmentation model, a Gaussian Mixture Model (GMM), vessel extraction
using vesselness filter, iterative probabilistic enhancement of vesselness via
neighborhood maximization and a distance-based weighting scheme over the
vessels. Experimental results demonstrate that our pipeline effectively
identifies Comb Sign, offering an objective, accurate, and reliable tool to
enhance diagnostic accuracy in Crohn's disease and related hypervascular
conditions where Comb Sign is considered as one of the important biomarkers.
