Medical Imaging 2020: Computer-Aided Diagnosis 2020
DOI: 10.1117/12.2548910
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Visualizing intestines for diagnostic assistance of ileus based on intestinal region segmentation from 3D CT images

Abstract: This paper presents a visualization method of intestine (the small and large intestines) 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 case… Show more

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Cited by 8 publications
(3 citation statements)
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“…Since the inner cylinder was free of contact with sub-adjacent anatomy, a cylindrical shape constraint applied on this augmented branch guided the network to generate a topologically correct segmentation on both qualitative and quantitative evaluation. Similarly, it has been demonstrated that a 3D U-Net could be trained for fully automated small bowel segmentation in patients with ileus even with sparsely annotated CT datasets [79].…”
Section: Application Of Digital Technologies To Small Bowel Imagingmentioning
confidence: 99%
“…Since the inner cylinder was free of contact with sub-adjacent anatomy, a cylindrical shape constraint applied on this augmented branch guided the network to generate a topologically correct segmentation on both qualitative and quantitative evaluation. Similarly, it has been demonstrated that a 3D U-Net could be trained for fully automated small bowel segmentation in patients with ileus even with sparsely annotated CT datasets [79].…”
Section: Application Of Digital Technologies To Small Bowel Imagingmentioning
confidence: 99%
“…Up to now, researchers have presented some methods for intestine segmentation task. [4][5][6][7] U-Net and its various optimization methods get good results in organ segmentation tasks. However, those methods just use pixel-level labels for training, which is time-consuming for medical images.…”
Section: Introductionmentioning
confidence: 99%
“…There have been research efforts to develop automatic methods for small bowel segmentation for the last decade. [3][4][5][6] Considering the high difficulty of labeling the small bowel, the recent works based on deep learning presented data-efficient methods each by training with sparsely annotated CT volumes, 4 by incorporating a cylindrical shape prior, 5 or by developing an unsupervised domain adaptation technique for the small bowel. 6 While the segmentation is useful for detecting lesions, blockages, and for distinguishing the bowels from adjacent lesions in the mesentery, it may be insufficient to identify the whole structure of the small bowel especially for the parts with lumpy segmentation due to the aforementioned touching issue (Fig.…”
Section: Introductionmentioning
confidence: 99%