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Liu S, Fan J, Zang L, Yang Y, Fu T, Song H, Wang Y, Yang J. Pose estimation via structure-depth information from monocular endoscopy images sequence. BIOMEDICAL OPTICS EXPRESS 2024; 15:460-478. [PMID: 38223180 PMCID: PMC10783895 DOI: 10.1364/boe.498262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 12/08/2023] [Accepted: 12/14/2023] [Indexed: 01/16/2024]
Abstract
Image-based endoscopy pose estimation has been shown to significantly improve the visualization and accuracy of minimally invasive surgery (MIS). This paper proposes a method for pose estimation based on structure-depth information from a monocular endoscopy image sequence. Firstly, the initial frame location is constrained using the image structure difference (ISD) network. Secondly, endoscopy image depth information is used to estimate the pose of sequence frames. Finally, adaptive boundary constraints are used to optimize continuous frame endoscopy pose estimation, resulting in more accurate intraoperative endoscopy pose estimation. Evaluations were conducted on publicly available datasets, with the pose estimation error in bronchoscopy and colonoscopy datasets reaching 1.43 mm and 3.64 mm, respectively. These results meet the real-time requirements of various scenarios, demonstrating the capability of this method to generate reliable pose estimation results for endoscopy images and its meaningful applications in clinical practice. This method enables accurate localization of endoscopy images during surgery, assisting physicians in performing safer and more effective procedures.
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Affiliation(s)
- Shiyuan Liu
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
- China Center for Information Industry Development, Beijing 100081, China
| | - Jingfan Fan
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
| | - Liugeng Zang
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
| | - Yun Yang
- Department of General Surgery, Beijing Friendship Hospital, Capital Medical University; National Clinical Research Center for Digestive Diseases, Beijing 100050, China
| | - Tianyu Fu
- Institute of Engineering Medicine, Beijing Institute of Technology, Beijing 100081, China
| | - Hong Song
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Yongtian Wang
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
| | - Jian Yang
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
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2
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Marx C, Wulff P, Fink C, Baumgarten D. Optical Measurement of Ligament Strain: Opportunities and Limitations for Intraoperative Application. SENSORS (BASEL, SWITZERLAND) 2023; 23:7487. [PMID: 37687943 PMCID: PMC10490667 DOI: 10.3390/s23177487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 08/23/2023] [Accepted: 08/26/2023] [Indexed: 09/10/2023]
Abstract
A feasible and precise method to measure ligament strain during surgical interventions could significantly enhance the quality of ligament reconstructions. However, all existing scientific approaches to measure in vivo ligament strain possess at least one significant disadvantage, such as the impairment of the anatomical structure. Seeking a more advantageous method, this paper proposes defining medical and technical requirements for a non-destructive, optical measurement technique. Furthermore, we offer a comprehensive review of current optical endoscopic techniques which could potentially be suitable for in vivo ligament strain measurement, along with the most suitable optical measurement techniques. The most promising options are rated based on the defined explicit and implicit requirements. Three methods were identified as promising candidates for a precise optical measurement of the alteration of a ligaments strain: confocal chromatic imaging, shearography, and digital image correlation.
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Affiliation(s)
- Christian Marx
- Research Unit for Orthopedic Sports Medicine and Injury Prevention, UMIT TIROL—Private University for Health Sciences and Health Technology, 6060 Hall in Tirol, Austria; (C.M.); (C.F.)
| | - Paul Wulff
- Chair of Mechatronics and Machine Dynamics, Technische Universität Berlin, 10623 Berlin, Germany
| | - Christian Fink
- Research Unit for Orthopedic Sports Medicine and Injury Prevention, UMIT TIROL—Private University for Health Sciences and Health Technology, 6060 Hall in Tirol, Austria; (C.M.); (C.F.)
| | - Daniel Baumgarten
- Institute of Electrical and Biomedical Engineering, UMIT TIROL—Private University for Health Sciences and Health Technology, 6060 Hall in Tirol, Austria
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3
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Liu S, Fan J, Song D, Fu T, Lin Y, Xiao D, Song H, Wang Y, Yang J. Joint estimation of depth and motion from a monocular endoscopy image sequence using a multi-loss rebalancing network. BIOMEDICAL OPTICS EXPRESS 2022; 13:2707-2727. [PMID: 35774318 PMCID: PMC9203100 DOI: 10.1364/boe.457475] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 04/01/2022] [Accepted: 04/01/2022] [Indexed: 06/15/2023]
Abstract
Building an in vivo three-dimensional (3D) surface model from a monocular endoscopy is an effective technology to improve the intuitiveness and precision of clinical laparoscopic surgery. This paper proposes a multi-loss rebalancing-based method for joint estimation of depth and motion from a monocular endoscopy image sequence. The feature descriptors are used to provide monitoring signals for the depth estimation network and motion estimation network. The epipolar constraints of the sequence frame is considered in the neighborhood spatial information by depth estimation network to enhance the accuracy of depth estimation. The reprojection information of depth estimation is used to reconstruct the camera motion by motion estimation network with a multi-view relative pose fusion mechanism. The relative response loss, feature consistency loss, and epipolar consistency loss function are defined to improve the robustness and accuracy of the proposed unsupervised learning-based method. Evaluations are implemented on public datasets. The error of motion estimation in three scenes decreased by 42.1%,53.6%, and 50.2%, respectively. And the average error of 3D reconstruction is 6.456 ± 1.798mm. This demonstrates its capability to generate reliable depth estimation and trajectory reconstruction results for endoscopy images and meaningful applications in clinical.
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Affiliation(s)
- Shiyuan Liu
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
| | - Jingfan Fan
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
| | - Dengpan Song
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
| | - Tianyu Fu
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
| | - Yucong Lin
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
| | - Deqiang Xiao
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
| | - Hong Song
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China
| | - Yongtian Wang
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
| | - Jian Yang
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
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4
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Vohra N, Liu H, Nelson AH, Bailey K, El-Shenawee M. Hyperspectral terahertz imaging and optical clearance for cancer classification in breast tumor surgical specimen. J Med Imaging (Bellingham) 2022; 9:014002. [PMID: 35036473 PMCID: PMC8752447 DOI: 10.1117/1.jmi.9.1.014002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 12/21/2021] [Indexed: 01/14/2023] Open
Abstract
Purpose: We investigate the enhancement in terahertz (THz) images of freshly excised breast tumors upon treatment with an optical clearance agent. The hyperspectral imaging and spectral classifications are used to quantitatively demonstrate the image enhancement. Glycerol solution with 60% concentration is applied to excised breast tumor specimens for various time durations to investigate the effectiveness on image enhancement. Approach: THz reflection spectroscopy is utilized to obtain the absorption coefficient and the index of refraction of untreated and glycerol-treated tissues at each frequency up to 3 THz. Two classifiers, spectral angular mapping (SAM) based on several kernels and Euclidean minimum distance (EMD) are implemented to evaluate the effectiveness of the treatment. The testing raw data is obtained from five breast cancer specimens: two untreated specimens and three specimens treated with glycerol solution for 20, 40, or 60 min. All tumors used in the testing data have healthy tissues adjacent to cancerous ones consistent with the challenge faced in lumpectomy surgeries. Results: The glycerol-treated tissues showed a decrease in the absorption coefficients compared with untreated tissues, especially as the period of treatment increased. Although the sensitivity metric of the classifier presented higher values in the untreated tissues compared with the treated ones, the specificity and accuracy metrics demonstrated higher values for the treated tissues compared with the untreated ones. Conclusions: The biocompatible glycerol solution is a potential optical clearance agent in THz imaging while keeping the histopathology imaging intact. The SAM technique provided a good classification of cancerous tissues despite the small amount of cancer in the training data (only 7%). The SAM exponential kernel and EMD presented classification accuracy of ∼ 80 % to 85% compared with linear and polynomial kernels that provided accuracy ranging from 70% to 80%. Overall, glycerol treatment provides a potential improvement in cancer classification in freshly excised breast tumors.
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Affiliation(s)
- Nagma Vohra
- University of Arkansas, Department of Electrical Engineering, Fayetteville, Arkansas, United States
| | - Haoyan Liu
- University of Arkansas, Department of Computer Science and Engineering, Fayetteville, Arkansas, United States
| | - Alexander H. Nelson
- University of Arkansas, Department of Computer Science and Engineering, Fayetteville, Arkansas, United States
| | - Keith Bailey
- Charles River Laboratory, Mattawan, Michigan, United States
| | - Magda El-Shenawee
- University of Arkansas, Department of Electrical Engineering, Fayetteville, Arkansas, United States
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Cavalcanti TC, Lew HM, Lee K, Lee SY, Park MK, Hwang JY. Intelligent smartphone-based multimode imaging otoscope for the mobile diagnosis of otitis media. BIOMEDICAL OPTICS EXPRESS 2021; 12:7765-7779. [PMID: 35003865 PMCID: PMC8713661 DOI: 10.1364/boe.441590] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 11/10/2021] [Accepted: 11/10/2021] [Indexed: 06/14/2023]
Abstract
Otitis media (OM) is one of the most common ear diseases in children and a common reason for outpatient visits to medical doctors in primary care practices. Adhesive OM (AdOM) is recognized as a sequela of OM with effusion (OME) and often requires surgical intervention. OME and AdOM exhibit similar symptoms, and it is difficult to distinguish between them using a conventional otoscope in a primary care unit. The accuracy of the diagnosis is highly dependent on the experience of the examiner. The development of an advanced otoscope with less variation in diagnostic accuracy by the examiner is crucial for a more accurate diagnosis. Thus, we developed an intelligent smartphone-based multimode imaging otoscope for better diagnosis of OM, even in mobile environments. The system offers spectral and autofluorescence imaging of the tympanic membrane using a smartphone attached to the developed multimode imaging module. Moreover, it is capable of intelligent analysis for distinguishing between normal, OME, and AdOM ears using a machine learning algorithm. Using the developed system, we examined the ears of 69 patients to assess their performance for distinguishing between normal, OME, and AdOM ears. In the classification of ear diseases, the multimode system based on machine learning analysis performed better in terms of accuracy and F1 scores than single RGB image analysis, RGB/fluorescence image analysis, and the analysis of spectral image cubes only, respectively. These results demonstrate that the intelligent multimode diagnostic capability of an otoscope would be beneficial for better diagnosis and management of OM.
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Affiliation(s)
- Thiago C Cavalcanti
- Department of Information and Communication Engineering, Daegu Gyeongbuk Institute of Science and Technology, Daegu, Republic of Korea
| | - Hah Min Lew
- Department of Information and Communication Engineering, Daegu Gyeongbuk Institute of Science and Technology, Daegu, Republic of Korea
| | - Kyungsu Lee
- Department of Information and Communication Engineering, Daegu Gyeongbuk Institute of Science and Technology, Daegu, Republic of Korea
| | - Sang-Yeon Lee
- Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Hospital, Seoul, Republic of Korea
| | - Moo Kyun Park
- Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Hospital, Seoul, Republic of Korea
- co-first authors
| | - Jae Youn Hwang
- Department of Information and Communication Engineering, Daegu Gyeongbuk Institute of Science and Technology, Daegu, Republic of Korea
- co-first authors
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Du W, Rao N, Dong C, Wang Y, Hu D, Zhu L, Zeng B, Gan T. Automatic classification of esophageal disease in gastroscopic images using an efficient channel attention deep dense convolutional neural network. BIOMEDICAL OPTICS EXPRESS 2021; 12:3066-3081. [PMID: 34221645 DOI: 10.1364/boe.420935] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 04/07/2021] [Accepted: 04/25/2021] [Indexed: 02/05/2023]
Abstract
The accurate diagnosis of various esophageal diseases at different stages is crucial for providing precision therapy planning and improving 5-year survival rate of esophageal cancer patients. Automatic classification of various esophageal diseases in gastroscopic images can assist doctors to improve the diagnosis efficiency and accuracy. The existing deep learning-based classification method can only classify very few categories of esophageal diseases at the same time. Hence, we proposed a novel efficient channel attention deep dense convolutional neural network (ECA-DDCNN), which can classify the esophageal gastroscopic images into four main categories including normal esophagus (NE), precancerous esophageal diseases (PEDs), early esophageal cancer (EEC) and advanced esophageal cancer (AEC), covering six common sub-categories of esophageal diseases and one normal esophagus (seven sub-categories). In total, 20,965 gastroscopic images were collected from 4,077 patients and used to train and test our proposed method. Extensive experiments results have demonstrated convincingly that our proposed ECA-DDCNN outperforms the other state-of-art methods. The classification accuracy (Acc) of our method is 90.63% and the averaged area under curve (AUC) is 0.9877. Compared with other state-of-art methods, our method shows better performance in the classification of various esophageal disease. Particularly for these esophageal diseases with similar mucosal features, our method also achieves higher true positive (TP) rates. In conclusion, our proposed classification method has confirmed its potential ability in a wide variety of esophageal disease diagnosis.
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Affiliation(s)
- Wenju Du
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Nini Rao
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.,
| | - Changlong Dong
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yingchun Wang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Dingcan Hu
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Linlin Zhu
- Digestive Endoscopic Center of West China Hospital, Sichuan University, Chengdu 610017, China
| | - Bing Zeng
- School of Information and Communication Engineering, University Electronic Science and Technology of China, Chengdu 610054, China
| | - Tao Gan
- Digestive Endoscopic Center of West China Hospital, Sichuan University, Chengdu 610017, China.,
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7
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Kim J, Lew HM, Kim JH, Youn S, Faruque HA, Seo AN, Park SY, Chang JH, Kim E, Hwang JY. Forward-Looking Multimodal Endoscopic System Based on Optical Multispectral and High-Frequency Ultrasound Imaging Techniques for Tumor Detection. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:594-606. [PMID: 33079654 DOI: 10.1109/tmi.2020.3032275] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
We developed a forward-looking (FL) multimodal endoscopic system that offers color, spectral classified, high-frequency ultrasound (HFUS) B-mode, and integrated backscattering coefficient (IBC) images for tumor detection in situ. Examination of tumor distributions from the surface of the colon to deeper inside is essential for determining a treatment plan of cancer. For example, the submucosal invasion depth of tumors in addition to the tumor distributions on the colon surface is used as an indicator of whether the endoscopic dissection would be operated. Thus, we devised the FL multimodal endoscopic system to offer information on the tumor distribution from the surface to deep tissue with high accuracy. This system was evaluated with bilayer gelatin phantoms which have different properties at each layer of the phantom in a lateral direction. After evaluating the system with phantoms, it was employed to characterize forty human colon tissues excised from cancer patients. The proposed system could allow us to obtain highly resolved chemical, anatomical, and macro-molecular information on excised colon tissues including tumors, thus enhancing the detection of tumor distributions from the surface to deep tissue. These results suggest that the FL multimodal endoscopic system could be an innovative screening instrument for quantitative tumor characterization.
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8
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Liu C, Shi C, Wang T, Zhang H, Jing L, Jin X, Xu J, Wang H. Bio-inspired multimodal 3D endoscope for image-guided and robotic surgery. OPTICS EXPRESS 2021; 29:145-157. [PMID: 33362105 DOI: 10.1364/oe.410424] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 11/19/2020] [Indexed: 06/12/2023]
Abstract
Image-guided and robotic surgery based on endoscopic imaging technologies can enhance cancer treatment by ideally removing all cancerous tissue and avoiding iatrogenic damage to healthy tissue. Surgeons evaluate the tumor margins at the cost of impeding surgical workflow or working with dimmed surgical illumination, since current endoscopic imaging systems cannot simultaneous and real-time color and near-infrared (NIR) fluorescence imaging under normal surgical illumination. To overcome this problem, a bio-inspired multimodal 3D endoscope combining the excellent characteristics of human eyes and compound eyes of mantis shrimp is proposed. This 3D endoscope, which achieves simultaneous and real-time imaging of three-dimensional stereoscopic, color, and NIR fluorescence, consists of three parts: a broad-band binocular optical system like as human eye, an optical relay system, and a multiband sensor inspired by the mantis shrimp's compound eye. By introducing an optical relay system, the two sub-images after the broad-band binocular optical system can be projected onto one and the same multiband sensor. A series of experiments demonstrate that this bio-inspired multimodal 3D endoscope not only provides surgeons with real-time feedback on the location of tumor tissue and lymph nodes but also creates an immersive experience for surgeons without impeding surgical workflow. Its excellent characteristics and good scalability can promote the further development and application of image-guided and robotic surgery.
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Shandro BM, Emrith K, Slabaugh G, Poullis A, Smith ML. Optical imaging technology in colonoscopy: Is there a role for photometric stereo? World J Gastrointest Endosc 2020; 12:138-148. [PMID: 32477448 PMCID: PMC7243575 DOI: 10.4253/wjge.v12.i5.138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Revised: 05/08/2020] [Accepted: 05/12/2020] [Indexed: 02/06/2023] Open
Abstract
Colonoscopy screening for the detection and removal of colonic adenomas is central to efforts to reduce the morbidity and mortality of colorectal cancer. However, up to a third of adenomas may be missed at colonoscopy, and the majority of post-colonoscopy colorectal cancers are thought to arise from these. Adenomas have three-dimensional surface topographic features that differentiate them from adjacent normal mucosa. However, these topographic features are not enhanced by white light colonoscopy, and the endoscopist must infer these from two-dimensional cues. This may contribute to the number of missed lesions. A variety of optical imaging technologies have been developed commercially to enhance surface topography. However, existing techniques enhance surface topography indirectly, and in two dimensions, and the evidence does not wholly support their use in routine clinical practice. In this narrative review, co-authored by gastroenterologists and engineers, we summarise the evidence for the impact of established optical imaging technologies on adenoma detection rate, and review the development of photometric stereo (PS) for colonoscopy. PS is a machine vision technique able to capture a dense array of surface normals to render three-dimensional reconstructions of surface topography. This imaging technique has several potential clinical applications in colonoscopy, including adenoma detection, polyp classification, and facilitating polypectomy, an inherently three-dimensional task. However, the development of PS for colonoscopy is at an early stage. We consider the progress that has been made with PS to date and identify the obstacles that need to be overcome prior to clinical application.
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Affiliation(s)
- Benjamin M Shandro
- Department of Gastroenterology, St George's University Hospitals NHS Foundation Trust, London SW17 0QT, United Kingdom
| | - Khemraj Emrith
- Centre for Machine Vision, University of the West of England, Bristol BS16 1QY, United Kingdom
| | - Gregory Slabaugh
- Department of Computer Science, City, University of London, London EC1V 0HB, United Kingdom
| | - Andrew Poullis
- Department of Gastroenterology, St George's University Hospitals NHS Foundation Trust, London SW17 0QT, United Kingdom
| | - Melvyn L Smith
- Centre for Machine Vision, University of the West of England, Bristol BS16 1QY, United Kingdom
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