DocumentCode :
85670
Title :
Automated Abdominal Multi-Organ Segmentation With Subject-Specific Atlas Generation
Author :
Wolz, Robin ; Chu, Chris ; Misawa, K. ; Fujiwara, Masamichi ; Mori, Kazuo ; Rueckert, Daniel
Author_Institution :
Dept. of Comput., Imperial Coll. London, London, UK
Volume :
32
Issue :
9
fYear :
2013
fDate :
Sept. 2013
Firstpage :
1723
Lastpage :
1730
Abstract :
A robust automated segmentation of abdominal organs can be crucial for computer aided diagnosis and laparoscopic surgery assistance. Many existing methods are specialized to the segmentation of individual organs and 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 computed tomography (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. The final segmentation is obtained by applying an automatically learned intensity model in a graph-cuts optimization step, incorporating high-level spatial knowledge. The proposed approach allows to deal with high inter-subject variation while being flexible enough to be applied to different organs. We have evaluated the segmentation on a database of 150 manually segmented CT images. The achieved results compare well to state-of-the-art methods, that are usually tailored to more specific questions, with Dice overlap values of 94%, 93%, 70%, and 92% for liver, kidneys, pancreas, and spleen, respectively.
Keywords :
computerised tomography; image registration; image segmentation; kidney; liver; medical image processing; optimisation; Dice overlap values; abdominal computed tomography scans; atlas database; automated abdominal multiorgan segmentation; automatically learned intensity model; brain segmentation; computer aided diagnosis; general fully-automated method; graph-cuts optimization step; hierarchical atlas registration; high intersubject variation; high-level spatial knowledge; kidneys; laparoscopic surgery assistance; liver; manually segmented computed tomography images; multiatlas registration; pancreas; patch-based segmentation; position variability; shape variability; spleen; state-of-the-art methods; subject-specific atlas generation; target specific priors; weighting scheme; Computed tomography; Image segmentation; Indexes; Kidney; Liver; Probabilistic logic; Abdominal computed tomography (CT); graph cuts; hierarchical model; multi-atlas segmentation; patch-based segmentation; Adult; Aged; Aged, 80 and over; Databases, Factual; Female; Humans; Image Processing, Computer-Assisted; Kidney; Liver; Male; Middle Aged; Models, Biological; Pancreas; Radiography, Abdominal; Reproducibility of Results; Spleen; Tomography, X-Ray Computed;
fLanguage :
English
Journal_Title :
Medical Imaging, IEEE Transactions on
Publisher :
ieee
ISSN :
0278-0062
Type :
jour
DOI :
10.1109/TMI.2013.2265805
Filename :
6522848
Link To Document :
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