Automatic prone to supine haustral fold matching in CT colonography using a Markov random field model

Authors:
Thomas Hampshire;Holger Roth;Mingxing Hu;Darren Boone;Greg Slabaugh;Shonit Punwani;Steve Halligan;David Hawkes
Affiliations:
Centre for Medical Image Computing, University College London, London, UK;Centre for Medical Image Computing, University College London, London, UK;Centre for Medical Image Computing, University College London, London, UK;Department of Specialist Radiology, University College Hospital, London, UK;Medicsight PLC, London, UK;Department of Specialist Radiology, University College Hospital, London, UK;Department of Specialist Radiology, University College Hospital, London, UK;Centre for Medical Image Computing, University College London, London, UK
Venue:
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Year:
2011

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Abstract

CT colonography is routinely performed with the patient prone and supine to differentiate fixed colonic pathology from mobile faecal residue. We propose a novel method to automatically establish correspondence. Haustral folds are detected using a graph cut method applied to a surface curvature-based metric, where image patches are generated using endoluminal CT colonography surface rendering. The intensity difference between image pairs, along with additional neighbourhood information to enforce geometric constraints, are used with a Markov Random Field (MRF) model to estimate the fold labelling assignment. The method achieved fold matching accuracy of 83.1% and 88.5% with and without local colonic collapse. Moreover, it improves an existing surface-based registration algorithm, decreasing mean registration error from 9.7mm to 7.7mm in cases exhibiting collapse.