Automatic lumen detection and magnetic alignment control for magnetic-assisted capsule colonoscope system optimization

Abstract We developed a magnetic-assisted capsule colonoscope system with integration of computer vision-based object detection and an alignment control scheme. Two convolutional neural network models A and B for lumen identification were trained on an endoscopic dataset of 9080 images. In the lumen...

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Autores principales:	Sheng-Yang Yen, Hao-En Huang, Gi-Shih Lien, Chih-Wen Liu, Chia-Feng Chu, Wei-Ming Huang, Fat-Moon Suk
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8e26ad4b67144afba13eb517355a32fd
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spelling	oai:doaj.org-article:8e26ad4b67144afba13eb517355a32fd2021-12-02T13:17:42ZAutomatic lumen detection and magnetic alignment control for magnetic-assisted capsule colonoscope system optimization10.1038/s41598-021-86101-92045-2322https://doaj.org/article/8e26ad4b67144afba13eb517355a32fd2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86101-9https://doaj.org/toc/2045-2322Abstract We developed a magnetic-assisted capsule colonoscope system with integration of computer vision-based object detection and an alignment control scheme. Two convolutional neural network models A and B for lumen identification were trained on an endoscopic dataset of 9080 images. In the lumen alignment experiment, models C and D used a simulated dataset of 8414 images. The models were evaluated using validation indexes for recall (R), precision (P), mean average precision (mAP), and F1 score. Predictive performance was evaluated with the area under the P-R curve. Adjustments of pitch and yaw angles and alignment control time were analyzed in the alignment experiment. Model D had the best predictive performance. Its R, P, mAP, and F1 score were 0.964, 0.961, 0.961, and 0.963, respectively, when the area of overlap/area of union was at 0.3. In the lumen alignment experiment, the mean degrees of adjustment for yaw and pitch in 160 trials were 21.70° and 13.78°, respectively. Mean alignment control time was 0.902 s. Finally, we compared the cecal intubation time between semi-automated and manual navigation in 20 trials. The average cecal intubation time of manual navigation and semi-automated navigation were 9 min 28.41 s and 7 min 23.61 s, respectively. The automatic lumen detection model, which was trained using a deep learning algorithm, demonstrated high performance in each validation index.Sheng-Yang YenHao-En HuangGi-Shih LienChih-Wen LiuChia-Feng ChuWei-Ming HuangFat-Moon SukNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sheng-Yang Yen Hao-En Huang Gi-Shih Lien Chih-Wen Liu Chia-Feng Chu Wei-Ming Huang Fat-Moon Suk Automatic lumen detection and magnetic alignment control for magnetic-assisted capsule colonoscope system optimization
description	Abstract We developed a magnetic-assisted capsule colonoscope system with integration of computer vision-based object detection and an alignment control scheme. Two convolutional neural network models A and B for lumen identification were trained on an endoscopic dataset of 9080 images. In the lumen alignment experiment, models C and D used a simulated dataset of 8414 images. The models were evaluated using validation indexes for recall (R), precision (P), mean average precision (mAP), and F1 score. Predictive performance was evaluated with the area under the P-R curve. Adjustments of pitch and yaw angles and alignment control time were analyzed in the alignment experiment. Model D had the best predictive performance. Its R, P, mAP, and F1 score were 0.964, 0.961, 0.961, and 0.963, respectively, when the area of overlap/area of union was at 0.3. In the lumen alignment experiment, the mean degrees of adjustment for yaw and pitch in 160 trials were 21.70° and 13.78°, respectively. Mean alignment control time was 0.902 s. Finally, we compared the cecal intubation time between semi-automated and manual navigation in 20 trials. The average cecal intubation time of manual navigation and semi-automated navigation were 9 min 28.41 s and 7 min 23.61 s, respectively. The automatic lumen detection model, which was trained using a deep learning algorithm, demonstrated high performance in each validation index.
format	article
author	Sheng-Yang Yen Hao-En Huang Gi-Shih Lien Chih-Wen Liu Chia-Feng Chu Wei-Ming Huang Fat-Moon Suk
author_facet	Sheng-Yang Yen Hao-En Huang Gi-Shih Lien Chih-Wen Liu Chia-Feng Chu Wei-Ming Huang Fat-Moon Suk
author_sort	Sheng-Yang Yen
title	Automatic lumen detection and magnetic alignment control for magnetic-assisted capsule colonoscope system optimization
title_short	Automatic lumen detection and magnetic alignment control for magnetic-assisted capsule colonoscope system optimization
title_full	Automatic lumen detection and magnetic alignment control for magnetic-assisted capsule colonoscope system optimization
title_fullStr	Automatic lumen detection and magnetic alignment control for magnetic-assisted capsule colonoscope system optimization
title_full_unstemmed	Automatic lumen detection and magnetic alignment control for magnetic-assisted capsule colonoscope system optimization
title_sort	automatic lumen detection and magnetic alignment control for magnetic-assisted capsule colonoscope system optimization
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/8e26ad4b67144afba13eb517355a32fd
work_keys_str_mv	AT shengyangyen automaticlumendetectionandmagneticalignmentcontrolformagneticassistedcapsulecolonoscopesystemoptimization AT haoenhuang automaticlumendetectionandmagneticalignmentcontrolformagneticassistedcapsulecolonoscopesystemoptimization AT gishihlien automaticlumendetectionandmagneticalignmentcontrolformagneticassistedcapsulecolonoscopesystemoptimization AT chihwenliu automaticlumendetectionandmagneticalignmentcontrolformagneticassistedcapsulecolonoscopesystemoptimization AT chiafengchu automaticlumendetectionandmagneticalignmentcontrolformagneticassistedcapsulecolonoscopesystemoptimization AT weiminghuang automaticlumendetectionandmagneticalignmentcontrolformagneticassistedcapsulecolonoscopesystemoptimization AT fatmoonsuk automaticlumendetectionandmagneticalignmentcontrolformagneticassistedcapsulecolonoscopesystemoptimization
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Automatic lumen detection and magnetic alignment control for magnetic-assisted capsule colonoscope system optimization

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