Multi-organ abdominal CT segmentation using hierarchically weighted subject-specific atlases

Authors:
Robin Wolz;Chengwen Chu;Kazunari Misawa;Kensaku Mori;Daniel Rueckert
Affiliations:
Imperial College London, London, UK;Department of Media Science, Nagoya University, Nagoya, Japan;Aichi Cancer Center, Nagoya, Japan;Department of Media Science, Nagoya University, Nagoya, Japan, Information and Communications Headquarters, Nagoya University, Japan;Imperial College London, London, UK
Venue:
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Year:
2012

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Abstract

A robust automated segmentation of abdominal organs can be crucial for computer aided diagnosis and laparoscopic surgery assistance. Many existing methods are specialised to the segmentation of individual organs or struggle to deal with the variability of the shape and position of abdominal organs. We present a general, fully-automated method for multi-organ segmentation of abdominal CT scans. The method is based on a hierarchical atlas registration and weighting scheme that generates target specific priors from an atlas database by combining aspects from multi-atlas registration and patch-based segmentation, two widely used methods in brain segmentation. This approach allows to deal with high inter-subject variation while being flexible enough to be applied to different organs. Our results on a dataset of 100 CT scans compare favourable to the state-of-the-art with Dice overlap values of 94%, 91%, 66% and 94% for liver, spleen, pancreas and kidney respectively.