Automatic colon segmentation with dual scan CT colonography

Abstract

We present a fully automated three-dimensional (3-D) segmentation algorithm to extract the colon lumen surface in CT colonography. Focusing on significant-size polyp detection, we target at an efficient algorithm that maximizes overall colon coverage, minimizes the extracolonic components, maintains local shape accuracy, and achieves high segmentation speed. Two-dimensional (2-D) image processing techniques are employed first, resulting in automatic seed placement and better colon coverage. This is followed by near-air threshold 3-D region-growing using an improved marching-cubes algorithm, which provides fast and accurate surface generation. The algorithm constructs a well-organized vertex-triangle structure that uniquely employs a hash table method, yielding an order of magnitude speed improvement. We segment two scans, prone and supine, independently and with the goal of improved colon coverage. Both segmentations would be available for subsequent polyp detection systems. Segmenting and analyzing both scans improves surface coverage by at least 6% over supine or prone alone. According to subjective evaluation, the average coverage is about 87.5% of the entire colon. Employing near-air threshold and elongation criteria, only 6% of the data sets include extracolonic components (EC) in the segmentation. The observed surface shape accuracy of the segmentation is adequate for significant-size (6 mm) polyp detection, which is also verified by the results of the prototype detection algorithm. The segmentation takes less than 5 minutes on an AMD 1-GHz single-processor PC, which includes reading the volume data and writing the surface results. The surface-based segmentation algorithm is practical for subsequent polyp detection algorithms in that it produces high coverage, has a low EC rate, maintains local shape accuracy, and has a computational efficiency that makes real-time polyp detection possible. A fully automatic or computer-aided polyp detection system using this technique is likely to benefit future colon cancer early screening.

Figure 1

Typical 2-D CT slice with segmentation difficulties. The rectangular frame regions in (a) show thin walls with air on both sides; (b) has a part of colon buried in the residue fluid, which is highlighted with the dashed line. The rectangular frame regions in (c) are the collapsed parts in sigmoid colon.

Figure 2

Flow diagram of the segmentation. The two parts of the algorithm are separated by the double line.

Figure 3

The anatomical features in 2-D CTC slices: (a) shows a non-bottom slice of lungs. The lung segments are large when small nodules and bronchioles are removed with an appropriate threshold, such as -650 HU. Background regions are shown in (a)-(c), and they are either too large or non-oval in shape; (b) shows a bottom slice of lungs, and (c) is in the rectum section. No big air-inflated anatomic structures are apparent, and the axes of the colon are relatively large.

Figure 4

The result of the 2-D segmentation. The short arrows indicate the small air chambers or artifacts, and the long arrows are small bowel segments. The thick long arrow in (b) points out a C-shaped small bowel segment. In (a) and (b), the dark regions are the segments of the colon, only five of which pass the filters and result in the seeds shown at the cross points.

Figure 5

The histogram of middle-range slices from all data sets.

Figure 6

Surface difference between high and low thresholds: (a) uses near-air threshold, -814 HU; (b) uses a median threshold, -574 HU, but includes significant parts of small bowel.

Figure 7

Comparison of polyp (about 5-mm) areas segmented using different thresholds: (a) uses -814 HU, and (b) uses -574 HU. Both clearly show the polyp.

Figure 8

An example of marching-cubes: Points A-H are iso-points. Triangles of the marching-cubes are shown in gray.

Figure 9

Percent coverage of the segmentation result on 100 dual scan studies. The results for supine scans better than for prone scans and obviously improved while using both: (a) shows the results from all scans; (b) shows the results of no-contrast scans; and (c) shows the results from contrast scans.

Figure 10

Surface view of the segmentation results: (a) is a well-inflated colon that is fully segmented under a single scan; (b) shows another case of a fully segmented colon, which has thinner sigmoid, descending, and transverse sections and a small spike of artifact at the arrow position; (c) and (d) show a 97% segmentation by combining the two scans; (e) shows 45% coverage using dual scan; (f) shows a segmentation that includes small bowel due to missing wall.