Extraction of Key-Frames From Endoscopic Videos by Using Depth Information

Early detection of colorectal cancer (CRC) can reduce the risk of death. Polyps are the precursor to such cancer. Analyzing the polyps from the most significant frames out of thousands of endoscopy frames is vital for diagnosing and understanding disease. In this article, a deep learning-based monoc...

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Autores principales:	Pradipta Sasmal, Avinash Paul, M. K. Bhuyan, Yuji Iwahori, Kunio Kasugai
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Key-frames colorectal cancer (CRC) monocular depth polyps 3D reconstruction Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/baf3a8eb81334bb2b844e05ea3435d09
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spelling	oai:doaj.org-article:baf3a8eb81334bb2b844e05ea3435d092021-11-20T00:02:02ZExtraction of Key-Frames From Endoscopic Videos by Using Depth Information2169-353610.1109/ACCESS.2021.3126835https://doaj.org/article/baf3a8eb81334bb2b844e05ea3435d092021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9610026/https://doaj.org/toc/2169-3536Early detection of colorectal cancer (CRC) can reduce the risk of death. Polyps are the precursor to such cancer. Analyzing the polyps from the most significant frames out of thousands of endoscopy frames is vital for diagnosing and understanding disease. In this article, a deep learning-based monocular depth estimation (MDE) technique is proposed to select the most informative frames (key-frames) of an endoscopic video. In most cases, ground truth depth maps of polyps are not readily available, and that is why the transfer learning approach is adopted in our method. An endoscopic modality generally captures thousands of frames. In this scenario, it is quite essential to discard low-quality and clinically irrelevant frames of an endoscopic video while the most informative frames should be retained for clinical diagnosis. In this view, a key-frame selection strategy is proposed by utilizing the depth information of polyps. In our method, image moment, edge magnitude, and key points are considered for adaptively selecting the key-frames. One important application of our proposed method could be the 3D reconstruction of polyps with the help of extracted key-frames. It gives a surgeon a real-time 3D view of the polyp surface for resection which involves detaching the polyp from its mucosa layer. Also, polyps are localized with the help of extracted depth maps.Pradipta SasmalAvinash PaulM. K. BhuyanYuji IwahoriKunio KasugaiIEEEarticleKey-framescolorectal cancer (CRC)monocular depthpolyps3D reconstructionElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 153004-153011 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Key-frames colorectal cancer (CRC) monocular depth polyps 3D reconstruction Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Key-frames colorectal cancer (CRC) monocular depth polyps 3D reconstruction Electrical engineering. Electronics. Nuclear engineering TK1-9971 Pradipta Sasmal Avinash Paul M. K. Bhuyan Yuji Iwahori Kunio Kasugai Extraction of Key-Frames From Endoscopic Videos by Using Depth Information
description	Early detection of colorectal cancer (CRC) can reduce the risk of death. Polyps are the precursor to such cancer. Analyzing the polyps from the most significant frames out of thousands of endoscopy frames is vital for diagnosing and understanding disease. In this article, a deep learning-based monocular depth estimation (MDE) technique is proposed to select the most informative frames (key-frames) of an endoscopic video. In most cases, ground truth depth maps of polyps are not readily available, and that is why the transfer learning approach is adopted in our method. An endoscopic modality generally captures thousands of frames. In this scenario, it is quite essential to discard low-quality and clinically irrelevant frames of an endoscopic video while the most informative frames should be retained for clinical diagnosis. In this view, a key-frame selection strategy is proposed by utilizing the depth information of polyps. In our method, image moment, edge magnitude, and key points are considered for adaptively selecting the key-frames. One important application of our proposed method could be the 3D reconstruction of polyps with the help of extracted key-frames. It gives a surgeon a real-time 3D view of the polyp surface for resection which involves detaching the polyp from its mucosa layer. Also, polyps are localized with the help of extracted depth maps.
format	article
author	Pradipta Sasmal Avinash Paul M. K. Bhuyan Yuji Iwahori Kunio Kasugai
author_facet	Pradipta Sasmal Avinash Paul M. K. Bhuyan Yuji Iwahori Kunio Kasugai
author_sort	Pradipta Sasmal
title	Extraction of Key-Frames From Endoscopic Videos by Using Depth Information
title_short	Extraction of Key-Frames From Endoscopic Videos by Using Depth Information
title_full	Extraction of Key-Frames From Endoscopic Videos by Using Depth Information
title_fullStr	Extraction of Key-Frames From Endoscopic Videos by Using Depth Information
title_full_unstemmed	Extraction of Key-Frames From Endoscopic Videos by Using Depth Information
title_sort	extraction of key-frames from endoscopic videos by using depth information
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/baf3a8eb81334bb2b844e05ea3435d09
work_keys_str_mv	AT pradiptasasmal extractionofkeyframesfromendoscopicvideosbyusingdepthinformation AT avinashpaul extractionofkeyframesfromendoscopicvideosbyusingdepthinformation AT mkbhuyan extractionofkeyframesfromendoscopicvideosbyusingdepthinformation AT yujiiwahori extractionofkeyframesfromendoscopicvideosbyusingdepthinformation AT kuniokasugai extractionofkeyframesfromendoscopicvideosbyusingdepthinformation
_version_	1718419860625555456

Extraction of Key-Frames From Endoscopic Videos by Using Depth Information

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