Title :
Multilabel Region Classification and Semantic Linking for Colon Segmentation in CT Colonography
Author :
Xiaoyun Yang ; Xujiong Ye ; Slabaugh, Greg
Author_Institution :
Hometrack Data Syst. Ltd., UK
Abstract :
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.
Keywords :
biological organs; computerised tomography; feature extraction; graph theory; hierarchical systems; image classification; image registration; image segmentation; inference mechanisms; learning (artificial intelligence); medical image processing; random processes; semantic networks; 3D air-filled object decomposition; 3D colon connection; 3D object region modeling; 3D region set; AEC removal; CT colonography application; CT image; DEC removal; a priori topological constraint; air-filled organ; attached extracolonic component; automatic colon segmentation; classifier training; clinical application; colon 3D virtual flythrough; colon collapse case; colon polyp CAD; colon segmentation accuracy; computer-aided detection; detached extracolonic component; discriminative learning method; extra-colonic component categorization; extra-colonic component reduction; extracolonic component removal; global anatomic structure modeling; graph cut; graph inference problem; hierarchical conditional random field; lung; multiclass classifier; multilabel region classification; noncolon region identification; poor insufflation effect; prone-supine registration; region-level feature; segmentation quality; semantic information; semantic linking; small intestine; stomach; true colon region detection; Cancer; Colon; Computed tomography; Joining processes; Liquids; Semantics; Three-dimensional displays; CT colonography; CT colonography (CTC); graph inference; segmentation;
Journal_Title :
Biomedical Engineering, IEEE Transactions on
DOI :
10.1109/TBME.2014.2374355