Multilabel Region Classification and Semantic Linking for Colon Segmentation in CT Colonography

Accurate and automatic colon segmentation from CT images is a crucial step of many clinical applications in CT colonography, including computer-aided detection (CAD) of colon polyps, 3-D virtual flythrough of the colon, and prone/supine registration. However, the existence of adjacent air-filled org...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2015-03, Vol.62 (3), p.948-959
Main Authors: Yang, Xiaoyun, Ye, Xujiong, Slabaugh, Greg
Format: Article
Language:English
Subjects:
Algorithms
Cancer
Classifiers
Collapse
Colon
Colon - diagnostic imaging
Colonography, Computed Tomographic - methods
Computed tomography
CT colonography
graph inference
Graphs
Humans
Imaging, Three-Dimensional - methods
Inference
Joining processes
Liquids
Segmentation
Semantics
Three dimensional
Three-dimensional displays
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Summary:Accurate and automatic colon segmentation from CT images is a crucial step of many clinical applications in CT colonography, including computer-aided detection (CAD) of colon polyps, 3-D virtual flythrough of the colon, and prone/supine registration. However, the existence of adjacent air-filled organs such as the lung, stomach, and small intestine, and the collapse of the colon due to poor insufflation, render accurate segmentation of the colon a difficult problem. Extra-colonic components can be categorized into two types based on their 3-D connection to the colon: detached and attached extracolonic components (DEC and AEC, respectively). In this paper, we propose graph inference methods to remove extracolonic components to achieve a high quality segmentation. We first decompose each 3-D air-filled object into a set of 3-D regions. A classifier trained with region-level features can be used to identify the colon regions from noncolon regions. After removing obvious DEC, we remove the remaining DEC by modeling the global anatomic structure with an a priori topological constraint and solving a graph inference problem using semantic information provided by a multiclass classifier. Finally, we remove AEC by modeling regions within each 3-D object with a hierarchical conditional random field, solved by graph cut. Experimental results demonstrate that our method outperforms a purely discriminative learning method in detecting true colon regions, while decreasing extra-colonic components in challenging clinical data that includes collapsed cases.
ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2014.2374355