Segmentation of the musculature is very important for accurate organ segmentation, analysis of body composition, and localization of tumors in the muscle. In research fields of computer assisted surgery and computer-aided diagnosis
(CAD), muscle segmentation in CT images is a necessary pre-processing step. This task is particularly challenging due
to the large variability in muscle structure and the overlap in intensity between muscle and internal organs. This problem has not been solved completely, especially for all of thoracic, abdominal and pelvic regions. We propose an automated system to segment the musculature on CT scans. The method combines an atlas-based model, an active contour model and prior segmentation of fat and bones. First, body contour, fat and bones are segmented using existing methods. Second, atlas-based models are pre-defined using anatomic knowledge at multiple key positions in the body to handle the large variability in muscle shape. Third, the atlas model is refined using active contour models (ACM) that are constrained using the pre-segmented bone and fat. Before refining using ACM, the initialized atlas model of next slice is updated using previous atlas. The muscle is segmented using threshold and smoothed in 3D volume space. Thoracic, abdominal and pelvic CT scans were used to evaluate our method, and five key position slices for each case were selected and manually labeled as the reference. Compared with the reference ground truth, the overlap ratio of true positives is 91.1%±3.5%, and that of false positives is 5.5%±4.2%.
Segmentation of the mesenteric vasculature has important applications for evaluation of the small bowel. In particular, it
may be useful for small bowel path reconstruction and precise localization of small bowel tumors such as carcinoid.
Segmentation of the mesenteric vasculature is very challenging, even for manual labeling, because of the low contrast
and tortuosity of the small blood vessels. Many vessel segmentation methods have been proposed. However, most of
them are designed for segmenting large vessels. We propose a semi-automated method to extract the mesenteric
vasculature on contrast-enhanced abdominal CT scans. First, the internal abdominal region of the body is automatically
identified. Second, the major vascular branches are segmented using a multi-linear vessel tracing method. Third, small
mesenteric vessels are segmented using multi-view multi-scale vesselness enhancement filters. The method is insensitive
to image contrast, variations of vessel shape and small occlusions due to overlapping. The method could automatically
detect mesenteric vessels with diameters as small as 1 mm. Compared with the standard-of-reference manually labeled
by an expert radiologist, the segmentation accuracy (recall rate) for the whole mesenteric vasculature was 82.3% with a
3.6% false positive rate.