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 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

A remote access CT colonography training system

Computed tomography colonography (CTC) is emerging as an alternative to conventional colonoscopy (CC). However CTC is not yet in widespread use due in part to the lack of suitably trained radiologists. We have developed a novel remote access system to train radiologists for colorectal cancer screening using CTC. To ensure that radiologists can gain the relevant experience without the need for any specialist equipment or software, we opted for designing a system that is accessible via the Internet using a standard browser. The interface lets the user locate and characterise polyps with the help of appropriate tools such as windowing, polyp measurement, zooming and a 3-D view. Each user has an account in order to allow monitoring of their training. They can also run an automatic evaluation of their work based on gold standard information previously gathered from specialists. This thesis also describes an initial implementation exclusively made up of Java Servlets. The evaluation of this system has been discussed in order to determine a better approach. The final system has been developed using a combination of Java Servlets and Applets. This approach offers fast response time to the user-interface. An iteration of lumen tracking using the system takes approximately 45 seconds. This research has yielded an operational system that meets the needs of remote access users