Real-Time 3D Reconstruction of Colonoscopic Surfaces for Determining Missing Regions

Ma, Rong-Na; Wang, Rui; Pizer, Stephen M.; Rosenman, Julian G.; McGill, Sarah; Frahm, Jan‐Michael

doi:10.1007/978-3-030-32254-0_64

Cited by 65 publications

(49 citation statements)

References 15 publications

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“…It has recently been shown that these approaches are able to infer dense and detailed depth maps in colonoscopy [45]. By fusing consecutive depth maps and simultaneously estimating the endoscope motion using geometric constraints, it has been demonstrated that long range colon sections could be reconstructed [46]. A similar approach has also been successfully applied to 3-D reconstruction of the sinus anatomy from endoscopic video so as to propose an alternative to CT scans – expensive procedures using ionizing radiation – for longitudinal monitoring of patients after nasal obstruction surgery [47].…”

Section: Computer-assisted Navigationmentioning

confidence: 99%

Computer Vision in the Surgical Operating Room

et al. 2020

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Background: Multiple types of surgical cameras are used in modern surgical practice and provide a rich visual signal that is used by surgeons to visualize the clinical site and make clinical decisions. This signal can also be used by artificial intelligence (AI) methods to provide support in identifying instruments, structures, or activities both in real-time during procedures and postoperatively for analytics and understanding of surgical processes. Summary: In this paper, we provide a succinct perspective on the use of AI and especially computer vision to power solutions for the surgical operating room (OR). The synergy between data availability and technical advances in computational power and AI methodology has led to rapid developments in the field and promising advances. Key Messages: With the increasing availability of surgical video sources and the convergence of technologiesaround video storage, processing, and understanding, we believe clinical solutions and products leveraging vision are going to become an important component of modern surgical capabilities. However, both technical and clinical challenges remain to be overcome to efficiently make use of vision-based approaches into the clinic.

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Section: Computer-assisted Navigationmentioning

confidence: 99%

Computer Vision in the Surgical Operating Room

et al. 2020

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“…As a colonoscopy study using depth estimation, Itoh et al [5], Nadeem, and Kaufman [11] use depth estimation for polyp detection. In addition, Freedman et al [6] and Ma et al [27] apply dense 3D reconstruction to measure non-search areas of colonoscopy. In addition, there are adversarial training network-based approaches [12,14] that make composite images resemble real medical images, and unsupervised depth estimation studies to be applied to wireless endoscopic capsules [26].…”

Section: Colonoscpy Depth Estimationmentioning

confidence: 99%

Unsupervised Monocular Depth Estimation for Colonoscope System Using Feedback Network

Hwang

Park

Kim

et al. 2021

Sensors

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A colonoscopy is a medical examination used to check disease or abnormalities in the large intestine. If necessary, polyps or adenomas would be removed through the scope during a colonoscopy. Colorectal cancer can be prevented through this. However, the polyp detection rate differs depending on the condition and skill level of the endoscopist. Even some endoscopists have a 90% chance of missing an adenoma. Artificial intelligence and robot technologies for colonoscopy are being studied to compensate for these problems. In this study, we propose a self-supervised monocular depth estimation using spatiotemporal consistency in the colon environment. It is our contribution to propose a loss function for reconstruction errors between adjacent predicted depths and a depth feedback network that uses predicted depth information of the previous frame to predict the depth of the next frame. We performed quantitative and qualitative evaluation of our approach, and the proposed FBNet (depth FeedBack Network) outperformed state-of-the-art results for unsupervised depth estimation on the UCL datasets.

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“…So far, it has not been easy to develop such AI systems; however, Ma et al 69 . have reported 3D‐reconstruction of colonoscopy images.…”

Section: Computer‐aided Quality Improvement Systemmentioning

confidence: 99%

“…blind spots during colonoscopy (e.g., systems that tell endoscopists what percentage of the mucosa has been visualized) could lead to greater ADR, thus reducing the incidence of CRCs. So far, it has not been easy to develop such AI systems; however, Ma et al 69 have reported 3D-reconstruction of colonoscopy images. They have developed a deep-learningdriven dense simultaneous localization and mapping system that can produce a camera trajectory and a dense reconstructed surface of the colon.…”

Section: Computer-aided Quality Improvement Systemmentioning

confidence: 99%

Current status and future perspective on artificial intelligence for lower endoscopy

Misawa

Kudo

Mori

et al. 2020

Digestive Endoscopy

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The global incidence and mortality rate of colorectal cancer remains high. Colonoscopy is regarded as the gold standard examination for detecting and eradicating neoplastic lesions. However, there are some uncertainties in colonoscopy practice that are related to limitations in human performance. First, approximately one-fourth of colorectal neoplasms are missed on a single colonoscopy. Second, it is still difficult for nonexperts to perform adequately regarding optical biopsy. Third, recording of some quality indicators (e.g. cecal intubation, bowel preparation, and withdrawal speed) which are related to adenoma detection rate, is sometimes incomplete. With recent improvements in machine learning techniques and advances in computer performance, artificial intelligence-assisted computeraided diagnosis is being increasingly utilized by endoscopists. In particular, the emergence of deep-learning, data-driven machine learning techniques have made the development of computer-aided systems easier than that of conventional machine learning techniques, the former currently being considered the standard artificial intelligence engine of computer-aided diagnosis by colonoscopy. To date, computer-aided detection systems seem to have improved the rate of detection of neoplasms. Additionally, computer-aided characterization systems may have the potential to improve diagnostic accuracy in real-time clinical practice. Furthermore, some artificial intelligence-assisted systems that aim to improve the quality of colonoscopy have been reported. The implementation of computer-aided system clinical practice may provide additional benefits such as helping in educational poorly performing endoscopists and supporting real-time clinical decision-making. In this review, we have focused on computer-aided diagnosis during colonoscopy reported by gastroenterologists and discussed its status, limitations, and future prospects.

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Real-Time 3D Reconstruction of Colonoscopic Surfaces for Determining Missing Regions

Cited by 65 publications

References 15 publications

Computer Vision in the Surgical Operating Room

Computer Vision in the Surgical Operating Room

Unsupervised Monocular Depth Estimation for Colonoscope System Using Feedback Network

Current status and future perspective on artificial intelligence for lower endoscopy

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