3D Colon Segment and Endoscope Motion Reconstruction from Colonoscopy Video

Chang, Carl K.; Hong, Dongho

doi:10.31274/etd-180810-580

Cited by 2 publications

(2 citation statements)

References 52 publications

(82 reference statements)

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“…Figure 3(c) shows the spiral score as the white text on the top right corner and three little green triangles indicating the quadrants that had been inspected. Hong et al improved the method for calculating spiral scores [38] based on detection of colon fold edges and the center of the innermost haustral fold. Feedback showing the spiral scores was used in a single center clinical trial on ten GI-trainees [39] over 159 colonoscopy procedures.…”

Section: B Bowel Preparation and Cleansingmentioning

confidence: 99%

Artificial Intelligence for Colonoscopy: Past, Present, and Future

Tavanapong

Riegler

et al. 2022

IEEE J. Biomed. Health Inform.

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During the past decades, many automated image analysis methods have been developed for colonoscopy. Realtime implementation of the most promising methods during colonoscopy has been tested in clinical trials, including several recent multi-center studies. All trials have shown results that may contribute to prevention of colorectal cancer. We summarize the past and present development of colonoscopy video analysis methods, focusing on two categories of artificial intelligence (AI) technologies used in clinical trials. These are (1) analysis and feedback for improving colonoscopy quality and (2) detection of abnormalities. Our survey includes methods that use traditional machine learning algorithms on carefully designed hand-crafted features as well as recent deep-learning methods. Lastly, we present the gap between current state-of-the-art technology and desirable clinical features and conclude with future directions of endoscopic AI technology development that will bridge the current gap.

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Section: B Bowel Preparation and Cleansingmentioning

confidence: 99%

Artificial Intelligence for Colonoscopy: Past, Present, and Future

Tavanapong

Riegler

et al. 2022

IEEE J. Biomed. Health Inform.

View full text Add to dashboard Cite

show abstract

“…[277], [278] proposed to use, affine invariant version of SIFT detector and descriptor to estimate the epipolar geometry and recover the 3D. In [279], [280], edges of colon fold contours were first detected and processed to generate the wire frame of the reconstructed virtual colon. A colon fold contour estimation algorithm using a single colonoscopy image was proposed and the depth and shape estimation of colon folds using brightness intensity of pixels was introduced.…”

Section: -[229] 10) Intra and Inter-operative Re-localization (Iao An...mentioning

confidence: 99%

Survey of Computer Vision and Machine Learning in Gastrointestinal Endoscopy

Vemuri

2019

Preprint

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3D Colon Segment and Endoscope Motion Reconstruction from Colonoscopy Video

Cited by 2 publications

References 52 publications

Artificial Intelligence for Colonoscopy: Past, Present, and Future

Artificial Intelligence for Colonoscopy: Past, Present, and Future

Survey of Computer Vision and Machine Learning in Gastrointestinal Endoscopy

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