This paper proposes an intestinal region reconstruction method from CT volumes of ileus cases. Binarized intestine segmentation results often contain incorrect contacts or loops. We utilize the 3D U-Net to estimate the distance map, which is high only at the centerlines of the intestines, to obtain regions around the centerlines. Watershed algorithm is utilized with local maximums of the distance maps as seeds for obtaining “intestine segments”. Those intestine segments are connected as graphs, for removing incorrect contacts and loops and to extract “intestine paths”, which represent how intestines are running. Experimental results using 19 CT volumes showed that our proposed method properly estimated intestine paths. These results were intuitively visualized for understanding the shape of the intestines and finding obstructions.
This paper presents a visualization method of intestine (the small and large intestine) regions and their stenosed parts caused by ileus from CT volumes. Since it is difficult for non-expert clinicians to find stenosed parts, the intestine and its stenosed parts should be visualized intuitively. Furthermore, the intestine regions of ileus cases are quite hard to be segmented. The proposed method segments intestine regions by 3D FCN (3D U-Net). Intestine regions are quite difficult to be segmented in ileus cases since the inside the intestine is filled with liquids. These liquids have similar intensities with intestinal wall on 3D CT volumes. We segment the intestine regions by using 3D U-Net trained by a weak annotation approach. Weak-annotation makes possible to train the 3D U-Net with small manually-traced label images of the intestine. This avoids us to prepare many annotation labels of the intestine that has long and winding shape. Each intestine segment is volume-rendered and colored based on the distance from its endpoint in volume rendering. Stenosed parts (disjoint points of an intestine segment) can be easily identified on such visualization. In the experiments, we showed that stenosed parts were intuitively visualized as endpoints of segmented regions, which are colored by red or blue.