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Updated: May 16, 2026

How to Measure Cortical Folding from MR Images: a Step-by-Step Tutorial to Compute Local Gyrification Index

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Haustral fold segmentation with curvature-guided level set evolution.

Hongbin Zhu¹, Matthew Barish, Perry Pickhardt

¹Department of Radiology, Stony Brook University, Stony Brook, NY 11794, USA. hbzhu@mil.sunysb.edu

IEEE Transactions on Bio-Medical Engineering

|November 30, 2012

Summary

This study presents an automated method for segmenting human colon haustral folds, improving shape analysis for computer-aided detection (CAD) of polyps. The novel level-set approach accurately maps fold surfaces, aiding computed tomographic colonography (CTC) analysis.

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Area of Science:

Medical Imaging
Computational Anatomy
Gastroenterology

Background:

Human colon anatomy features haustral folds, which are thin protrusions on the colon wall.
These folds complicate shape analysis, particularly for computer-aided detection (CAD) of colonic polyps.
Accurate segmentation of haustral folds can simplify colon structure analysis and serve as an anatomical reference in computed tomographic colonography (CTC).

Purpose of the Study:

To develop an automated level-set approach for segmenting haustral fold surfaces in the colon.
To evaluate the accuracy and effectiveness of the developed fold segmentation algorithm.
To assess the potential benefits of automatically segmented fold surfaces for post-procedural analysis in CTC.

Main Methods:

A level-set approach was developed based on a model of haustral fold boundaries.
Ground truth data were established by expert manual drawing of haustral fold boundaries on 15 patient CTC datasets.
Segmentation accuracy was evaluated using sensitivity and a novel merit of segmented-area ratio (SAR).

Main Results:

The automated segmentation algorithm successfully detected 92.7% of haustral folds compared to the ground truth.
The segmentation algorithm achieved an average SAR of 86.2%, indicating a good match with expert-drawn fold surfaces.
The results demonstrate a high degree of accuracy in automatically segmenting colon haustral fold surfaces.

Conclusions:

The developed level-set approach provides an effective method for automatic segmentation of human colon haustral folds.
Accurate fold segmentation can simplify complex colon structures, aiding in applications like computer-aided detection (CAD) of polyps.
Automatically segmented fold surfaces hold significant potential for improving various post-procedural analyses in computed tomographic colonography (CTC).