This paper proposes an intestine segmentation method to segment intestines from CT volumes for helping clinicians diagnose intestine obstruction. For large-scale labeled datasets, fully-supervised methods have shown superior results. However, medical image segmentation is usually difficult to achieve accurate prediction due to the limited number of labeled data available for training. To address this challenge, we introduce a novel multi-view symmetrical network (MVS-Net) for intestine segmentation and incorporate bidirectional teaching to utilize unlabeled datasets. Specifically, we design the MVS-Net, which can use different sizes of convolution kernels instead of a fixed kernel size, enabling the network to capture multi-scale features from images’ different perceptual fields and ensure segmentation accuracy. Additionally, the pseudo-labels are generated by bidirectional teaching, which can make the network captures semantic information from large-scale unlabeled data for increasing the training data. We repeated the experiment five times, and used the averaged result on the intestines dataset to represent the segmentation accuracy of the proposed method. The experimental results showed the average Dice was 78.86%, the average recall 84.50%, and the average precision 75.94%, respectively.
This paper proposes an intestine segmentation method on CT volume based on a multi-class prediction of intestinal content materials (ICMs). The mechanical intestinal obstruction and the ileus (non-mechanical intestinal obstruction) are diseases which disrupt the movement of ICMs. Although clinicians find the obstruction point that movement of intestinal contents is required on CT volumes, it is difficult for non-expert clinicians to find the obstruction point. We have studied a CADe system which presents obstruction candidates to users by segmentation of the intestines on CT volumes. Generation of incorrect shortcuts in segmentation results was partly reduced in our proposed method by introducing distance maps. However, incorrect shortcuts still remained between the regions filled by air. This paper proposes an improved intestine segmentation method from CT volumes. We introduce a multi-class segmentation of ICMs (air, liquid, and feces). Reduction of incorrect shortcut generation is specifically applied to air regions. Experiments using 110 CT volumes showed that our proposed method reduced incorrect shortcuts. Rates of segmented regions that are analyzed as running through the intestine were 59.6% and 62.4% for the previous and proposed methods, respectively. This result partly implies that our proposed method reduced production of incorrect shortcuts.
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.
Diffuse reflectance spectroscopy (DRS) has been extensively used for characterization of biological tissues as a noninvasive optical technique to evaluate the optical properties of tissue. We investigated a method for evaluating the reduced scattering coefficient μs′, the absorption coefficient μa, the tissue oxygen saturation StO2, and the reduction of heme aa3 in cytochrome c oxidase CcO of in vivo liver tissue using a single-reflectance fiber probe with two source-collector geometries. We performed in vivo recordings of diffuse reflectance spectra for exposed rat liver during the ischemia–reperfusion induced by the hepatic portal (hepatic artery, portal vein, and bile duct) occlusion. The time courses of μa at 500, 530, 570, and 584 nm indicated the hemodynamic change in liver tissue as well as StO2. Significant increase in μa(605)/μa(620) during ischemia and after euthanasia induced by nitrogen breathing was observed, which indicates the reduction of heme aa3, representing a sign of mitochondrial energy failure. The time courses of μs′ at 500, 530, 570, and 584 nm were well correlated with those of μa, which also reflect the scattering by red blood cells. On the other hand, at 700 and 800 nm, a temporary increase in μs′ and an irreversible decrease in μs′ were observed during ischemia–reperfusion and after euthanasia induced by nitrogen breathing, respectively. The change in μs′ in the near-infrared wavelength region during ischemia is indicative of the morphological changes in the cellular and subcellular structures induced by the ischemia, whereas that after euthanasia implies the hepatocyte vacuolation. The results of the present study indicate the potential application of the current DRS system for evaluating the pathophysiological conditions of in vivo liver tissue.
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