TY - GEN
T1 - Multi-organ abdominal CT segmentation using hierarchically weighted subject-specific atlases
AU - Wolz, Robin
AU - Chu, Chengwen
AU - Misawa, Kazunari
AU - Mori, Kensaku
AU - Rueckert, Daniel
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2012.
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84872589536&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-33415-3_2
DO - 10.1007/978-3-642-33415-3_2
M3 - Conference contribution
C2 - 23285529
AN - SCOPUS:84872589536
SN - 9783642334146
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 10
EP - 17
BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings
A2 - Ayache, Nicholas
A2 - Delingette, Herve
A2 - Golland, Polina
A2 - Mori, Kensaku
PB - Springer Verlag
T2 - 15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012
Y2 - 1 October 2012 through 5 October 2012
ER -