David R. Stelts

Computing the centerline of a colon: a robust and efficient method based on 3D skeletons.

We present a robust and efficient algorithm for calculating the centerline of a computer-generated colon model created from helical CT image data. The centerline is an essential aid for navigating through complex anatomy such as the colon. Our algorithm involves three steps. In the first step, we generate a 3D skeleton of the binary colon volume using a fast topological thinning algorithm. In the second step, we employ a graph search algorithm to remove extra loops and branches. These loops and branches are caused by holes in the object that are artifacts produced during image segmentation. In the final step, we compute a smooth representation of the centerline by approximating the skeleton with cubic B-splines. This final step is necessary because the skeleton contains many abrupt changes in direction due to the discrete nature of image data. The user supplies two endpoints for the centerline; otherwise, the algorithm is fully automated. Experimental results demonstrate that the algorithm is not only robust but also efficient.

3D Skeleton for Virtual Colonoscopy

This paper describes an improved algorithm for generating 3D skeletons from binary objects and its clinical application to virtual colonoscopy. A skeleton provides an ideal central path for an auto-piloted examination of a virtual colon rendered from a spiral computed tomography scan.

Virtual endoscopy: quicker and easier disease evaluation

The advent of spiral computed tomography (CT) has created the potential to image continuous anatomical volumes during a single breath-hold. The ability to reconstruct overlapping spiral CT images has improved through-plane resolution and contributed to improved diagnostic accuracy. When spiral CT is used to image organ systems such as the colon or airways, it is common to generate up to 500 CT images. We have developed a virtual endoscopy (VE) software system that couples computer-assisted diagnosis capabilities with volume visualization techniques to aid in the analysis of these large datasets. Despite its potential to assist in disease diagnosis, VE faces several important technical and nontechnical challenges that must be addressed before it becomes a clinical reality.

Computing the central path of colon lumen in Helical CT images

We present an efficient algorithm for calculating the central path of a computer-generated colon model created from helical computed tomography image data. The central path is an essential aid for navigating through complex anatomy such as the colon. Our algorithm involves three steps. In the first step, we generate a 3D skeleton of the binary colon volume using a fast topological thinning algorithm. In the second step, we employ a graph search algorithm to remove extra loops and branches. These loops and branches are caused by holes in the object which are artifacts produced during image segmentation. In the final step, we compute a smooth representation of the central path by approximating the skeleton with cubic B-splines. This final step is necessary because the skeleton contains many abrupt changes in direction due to the discrete nature of image data. The user supplies two endpoints for the central path; otherwise, the algorithm is fully automated. Experimental results demonstrate that the algorithm is not only efficient but also robust. Use of this method in virtual endoscopy systems should have widespread clinical implications.

Colorectal cancer screening with virtual colonoscopy

Early detection and removal of colorectal polyps have been proven to reduce mortality from colorectal carcinoma (CRC), the second leading cause of cancer deaths in the United States. Unfortunately, traditional techniques for CRC examination (i.e., barium enema, sigmoidoscopy, and colonoscopy) are unsuitable for mass screening because of either low accuracy or poor public acceptance, costs, and risks. Virtual colonoscopy (VC) is a minimally invasive alternative that is based on tomographic scanning of the colon. After a patients bowel is optimally cleansed and distended with gas, a fast tomographic scan, typically helical computed tomography (CT), of the abdomen is performed during a single breath-hold acquisition. Two-dimensional (2D) slices and three-dimensional (3D) rendered views of the colon lumen generated from the tomographic data are then examined for colorectal polyps. Recent clinical studies conducted at several institutions including ours have shown great potential for this technology to be an effective CRC screening tool. In this paper, we describe new methods to improve bowel preparation, colon lumen visualization, colon segmentation, and polyp detection. Our initial results show that VC with the new bowel preparation and imaging protocol is capable of achieving accuracy comparable to conventional colonoscopy and our new algorithms for image analysis contribute to increased accuracy and efficiency in VC examinations.