Mesenteric vasculature-guided small bowel segmentation on 3-D CT

Abstract

Due to its importance and possible applications in visualization, tumor detection and preoperative planning, automatic small bowel segmentation is essential for computer-aided diagnosis of small bowel pathology. However, segmenting the small bowel directly on computed tomography (CT) scans is very difficult because of the low image contrast on CT scans and high tortuosity of the small bowel and its close proximity to other abdominal organs. Motivated by the intensity characteristics of abdominal CT images, the anatomic relationship between the mesenteric vasculature and the small bowel, and potential usefulness of the mesenteric vasculature for establishing the path of the small bowel, we propose a novel mesenteric vasculature map-guided method for small bowel segmentation on high-resolution CT angiography scans. The major mesenteric arteries are first segmented using a vessel tracing method based on multi-linear subspace vessel model and Bayesian inference. Second, multi-view, multi-scale vesselness enhancement filters are used to segment small vessels, and vessels directly or indirectly connecting to the superior mesenteric artery are classified as mesenteric vessels. Third, a mesenteric vasculature map is built by linking vessel bifurcation points, and the small bowel is segmented by employing the mesenteric vessel map and fuzzy connectness. The method was evaluated on 11 abdominal CT scans of patients suspected of having carcinoid tumors with manually labeled reference standard. The result, 82.5% volume overlap accuracy compared with the reference standard, shows it is feasible to segment the small bowel on CT scans using the mesenteric vasculature as a roadmap.

Figures

Fig. 1

Anatomic configuration of mesenteric vasculature and intestines.

Fig. 2

3D models and the manually labeled paths of the mesenteric vasculature (yellow) and the small bowel (blue).

Fig. 3

System architecture

Fig. 4

An example of internal abdomen identification. From left to right and top to bottom: a) original CT slice, b) identification of body contour (yellow), SAT segmentation result (red) and spine segmentation result (pink), c) muscle segmentation result (blue), d) internal abdomen region (green).

Fig. 5

Initialization of vessel appearance model. The left one shows placing seeds (red points) on the middle cross section of a vessel segment, the right one shows generalized cylinder model for one seed.

Fig. 6

Demonstration of the procedure of major artery tracing. The blue points are seeds, blue lines present the evolution of seeds, and the red mask is the vessel tube for example data.

Fig. 7

Examples of line-like structure regions. The first row images are vessel regions, and the second row images are non-vessel regions. The last column images are intensity profile of regions.

Fig. 8

Mesenteric vessel segmentation. a) MIP image excluding external abdomen structures and bones, b) vessel enhancement using Frangi’s filters, c) vessel centerlines (red) after non-vessel removal, d) final vessel centerlines (red) after non-mesenteric vessel removal.

Fig. 9

Illustration of different reconnection procedures.

Fig. 10

Building the mesenteric vessel tree. a) Schematic showing bifurcation (3, 4) and non-bifurcation (1, 2, 5) points. b) Bifurcation point detection results for the image shown in Figure 8 (blue points; only part of Fig. 8 is shown for better display), (c) illustration of reconnection procedure (d) Build the vessel map by filling gaps (yellow) and localizing the extremas of the mesenteric vessel tree (pink), only procedure of several branches and extremas are shown, which is used to show the idea not the result.

Fig. 11

Small bowel boundary detection and region segmentation. The left one shows directions (arrows) of region growing away from extremal points. The right one shows the final small bowel segmentation.

Fig. 12

The accuracy of major artery tracing.

Fig. 13

The accuracy of small vessel enhancement result.

Fig. 14

The accuracy of small bowel segmentation. The top figure is the result by changing the thickness of slab for building MIP images. The lower one is the result by changing the threshold value in Frangi’s filter.

Fig. 15

The segmentation result of the small bowel on axial-view slices. The first row images are the original CT scans, the second row ones are the manually labeled small bowel regions (brown), and the third row ones are automatically segmented results of small bowel regions (brown). Regions specified by red rectangles indicate false positives in colon regions, and regions of black rectangles indicate false positives in small bowel regions.

Fig. 16

The segmentation result of the small bowel (brown) and the mesenteric vasculature (red) on 3D volume, the same case in anterior and posterior coronal views.

Fig. 17

The small bowel segmentation result using statistical model (SVM). The first column are original images, the second column are detection results.

Fig. 18

The segmentation result of the small bowel (brown) and the mesenteric vasculature (red) on 3D volume with manually setting the colon region, the same case in two views.