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Yang Z, Dai J, Pan J. 3D reconstruction from endoscopy images: A survey. Comput Biol Med 2024; 175:108546. [PMID: 38704902 DOI: 10.1016/j.compbiomed.2024.108546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 01/05/2024] [Accepted: 04/28/2024] [Indexed: 05/07/2024]
Abstract
Three-dimensional reconstruction of images acquired through endoscopes is playing a vital role in an increasing number of medical applications. Endoscopes used in the clinic are commonly classified as monocular endoscopes and binocular endoscopes. We have reviewed the classification of methods for depth estimation according to the type of endoscope. Basically, depth estimation relies on feature matching of images and multi-view geometry theory. However, these traditional techniques have many problems in the endoscopic environment. With the increasing development of deep learning techniques, there is a growing number of works based on learning methods to address challenges such as inconsistent illumination and texture sparsity. We have reviewed over 170 papers published in the 10 years from 2013 to 2023. The commonly used public datasets and performance metrics are summarized. We also give a taxonomy of methods and analyze the advantages and drawbacks of algorithms. Summary tables and result atlas are listed to facilitate the comparison of qualitative and quantitative performance of different methods in each category. In addition, we summarize commonly used scene representation methods in endoscopy and speculate on the prospects of deep estimation research in medical applications. We also compare the robustness performance, processing time, and scene representation of the methods to facilitate doctors and researchers in selecting appropriate methods based on surgical applications.
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Affiliation(s)
- Zhuoyue Yang
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, 37 Xueyuan Road, Haidian District, Beijing, 100191, China; Peng Cheng Lab, 2 Xingke 1st Street, Nanshan District, Shenzhen, Guangdong Province, 518000, China
| | - Ju Dai
- Peng Cheng Lab, 2 Xingke 1st Street, Nanshan District, Shenzhen, Guangdong Province, 518000, China
| | - Junjun Pan
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, 37 Xueyuan Road, Haidian District, Beijing, 100191, China; Peng Cheng Lab, 2 Xingke 1st Street, Nanshan District, Shenzhen, Guangdong Province, 518000, China.
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Mattille M, Boehler Q, Lussi J, Ochsenbein N, Moehrlen U, Nelson BJ. Autonomous Magnetic Navigation in Endoscopic Image Mosaics. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2400980. [PMID: 38482737 PMCID: PMC11109657 DOI: 10.1002/advs.202400980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Indexed: 05/23/2024]
Abstract
Endoscopes navigate within the human body to observe anatomical structures with minimal invasiveness. A major shortcoming of their use is their narrow field-of-view during navigation in large, hollow anatomical regions. Mosaics of endoscopic images can provide surgeons with a map of the tool's environment. This would facilitate procedures, improve their efficiency, and potentially generate better patient outcomes. The emergence of magnetically steered endoscopes opens the way to safer procedures and creates an opportunity to provide robotic assistance both in the generation of the mosaic map and in navigation within this map. This paper proposes methods to autonomously navigate magnetic endoscopes to 1) generate endoscopic image mosaics and 2) use these mosaics as user interfaces to navigate throughout the explored area. These are the first strategies, which allow autonomous magnetic navigation in large, hollow organs during minimally invasive surgeries. The feasibility of these methods is demonstrated experimentally both in vitro and ex vivo in the context of the treatment of twin-to-twin transfusion syndrome. This minimally invasive procedure is performed in utero and necessitates coagulating shared vessels of twin fetuses on the placenta. A mosaic of the vasculature in combination with autonomous navigation has the potential to significantly facilitate this challenging surgery.
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Affiliation(s)
- Michelle Mattille
- Multi‐Scale Robotics LabETH ZurichTannenstrasse 3Zurich8092Switzerland
| | - Quentin Boehler
- Multi‐Scale Robotics LabETH ZurichTannenstrasse 3Zurich8092Switzerland
| | - Jonas Lussi
- Multi‐Scale Robotics LabETH ZurichTannenstrasse 3Zurich8092Switzerland
| | - Nicole Ochsenbein
- Department of ObstetricsUniversity Hospital of ZurichRämistrasse 100Zürich8092Switzerland
- The Zurich Center for Fetal Diagnosis and TherapyUniversity of ZurichRämistrasse 71Zürich8092Switzerland
| | - Ueli Moehrlen
- The Zurich Center for Fetal Diagnosis and TherapyUniversity of ZurichRämistrasse 71Zürich8092Switzerland
- Department of Pediatric SurgeryUniversity Children's Hospital ZurichSteinwiesstrasse 75Zürich8092Switzerland
| | - Bradley J. Nelson
- Multi‐Scale Robotics LabETH ZurichTannenstrasse 3Zurich8092Switzerland
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Suzuki S, Monno Y, Arai R, Miyaoka M, Toya Y, Esaki M, Wada T, Hatta W, Takasu A, Nagao S, Ishibashi F, Minato Y, Konda K, Dohmen T, Miki K, Okutomi M. Diagnostic performance of deep-learning-based virtual chromoendoscopy in gastric neoplasms. Gastric Cancer 2024; 27:539-547. [PMID: 38240891 DOI: 10.1007/s10120-024-01469-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 01/09/2024] [Indexed: 04/15/2024]
Abstract
BACKGROUNDS Cycle-consistent generative adversarial network (CycleGAN) is a deep neural network model that performs image-to-image translations. We generated virtual indigo carmine (IC) chromoendoscopy images of gastric neoplasms using CycleGAN and compared their diagnostic performance with that of white light endoscopy (WLE). METHODS WLE and IC images of 176 patients with gastric neoplasms who underwent endoscopic resection were obtained. We used 1,633 images (911 WLE and 722 IC) of 146 cases in the training dataset to develop virtual IC images using CycleGAN. The remaining 30 WLE images were translated into 30 virtual IC images using the trained CycleGAN and used for validation. The lesion borders were evaluated by 118 endoscopists from 22 institutions using the 60 paired virtual IC and WLE images. The lesion area concordance rate and successful whole-lesion diagnosis were compared. RESULTS The lesion area concordance rate based on the pathological diagnosis in virtual IC was lower than in WLE (44.1% vs. 48.5%, p < 0.01). The successful whole-lesion diagnosis was higher in the virtual IC than in WLE images; however, the difference was insignificant (28.2% vs. 26.4%, p = 0.11). Conversely, subgroup analyses revealed a significantly higher diagnosis in virtual IC than in WLE for depressed morphology (41.9% vs. 36.9%, p = 0.02), differentiated histology (27.6% vs. 24.8%, p = 0.02), smaller lesion size (42.3% vs. 38.3%, p = 0.01), and assessed by expert endoscopists (27.3% vs. 23.6%, p = 0.03). CONCLUSIONS The diagnostic ability of virtual IC was higher for some lesions, but not completely superior to that of WLE. Adjustments are required to improve the imaging system's performance.
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Affiliation(s)
- Sho Suzuki
- Department of Gastroenterology, International University of Health and Welfare Ichikawa Hospital, 6-1-14, Konodai, Ichikawa-Shi, Chiba, 272-0827, Japan.
| | - Yusuke Monno
- Department of Systems and Control Engineering, School of Engineering, Tokyo Institute of Technology, Tokyo, Japan
| | - Ryo Arai
- Department of Systems and Control Engineering, School of Engineering, Tokyo Institute of Technology, Tokyo, Japan
| | - Masaki Miyaoka
- Department of Endoscopy, Fukuoka University Chikushi Hospital, Chikushino, Japan
| | - Yosuke Toya
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, School of Medicine, Iwate Medical University, Yahaba, Japan
| | - Mitsuru Esaki
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukouka, Japan
- Department of Gastroenterology, Harasanshin Hospital, Fukuoka, Japan
| | - Takuya Wada
- Department of Gastroenterology, Kitasato University School of Medicine, Sagamihara, Japan
| | - Waku Hatta
- Division of Gastroenterology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Ayaka Takasu
- Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Tokyo, Japan
| | - Shigeaki Nagao
- Medical Examination Center, Showa General Hospital, Tokyo, Japan
| | - Fumiaki Ishibashi
- Department of Gastroenterology, International University of Health and Welfare Ichikawa Hospital, 6-1-14, Konodai, Ichikawa-Shi, Chiba, 272-0827, Japan
- Endoscopy Center, Koganei Tsurukame Clinic, Tokyo, Japan
| | - Yohei Minato
- Department of Gastrointestinal Endoscopy, NTT Medical Center Tokyo, Tokyo, Japan
| | - Kenichi Konda
- Division of Gastroenterology, Department of Medicine, Showa University School of Medicine, Tokyo, Japan
| | - Takahiro Dohmen
- Department of Gastroenterology, Yuri Kumiai General Hospital, Yurihonjo, Japan
| | - Kenji Miki
- Department of Internal Medicine, Tsujinaka Hospital Kashiwanoha, Kashiwa, Japan
| | - Masatoshi Okutomi
- Department of Systems and Control Engineering, School of Engineering, Tokyo Institute of Technology, Tokyo, Japan
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Acar A, Lu D, Wu Y, Oguz I, Kavoussi N, Wu JY. Towards navigation in endoscopic kidney surgery based on preoperative imaging. Healthc Technol Lett 2024; 11:67-75. [PMID: 38638503 PMCID: PMC11022214 DOI: 10.1049/htl2.12059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 11/21/2023] [Indexed: 04/20/2024] Open
Abstract
Endoscopic renal surgeries have high re-operation rates, particularly for lower volume surgeons. Due to the limited field and depth of view of current endoscopes, mentally mapping preoperative computed tomography (CT) images of patient anatomy to the surgical field is challenging. The inability to completely navigate the intrarenal collecting system leads to missed kidney stones and tumors, subsequently raising recurrence rates. A guidance system is proposed to estimate the endoscope positions within the CT to reduce re-operation rates. A Structure from Motion algorithm is used to reconstruct the kidney collecting system from the endoscope videos. In addition, the kidney collecting system is segmented from CT scans using 3D U-Net to create a 3D model. The two collecting system representations can then be registered to provide information on the relative endoscope position. Correct reconstruction and localization of intrarenal anatomy and endoscope position is demonstrated. Furthermore, a 3D map is created supported by the RGB endoscope images to reduce the burden of mental mapping during surgery. The proposed reconstruction pipeline has been validated for guidance. It can reduce the mental burden for surgeons and is a step towards the long-term goal of reducing re-operation rates in kidney stone surgery.
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Affiliation(s)
- Ayberk Acar
- Department of Computer ScienceVanderbilt UniversityNashvilleTennesseeUSA
- Present address:
Department of Computer ScienceVanderbilt UniversityNashvilleTennesseeUSA
| | - Daiwei Lu
- Department of Computer ScienceVanderbilt UniversityNashvilleTennesseeUSA
| | - Yifan Wu
- Department of Computer ScienceVanderbilt UniversityNashvilleTennesseeUSA
| | - Ipek Oguz
- Department of Computer ScienceVanderbilt UniversityNashvilleTennesseeUSA
| | - Nicholas Kavoussi
- Department of UrologyVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Jie Ying Wu
- Department of Computer ScienceVanderbilt UniversityNashvilleTennesseeUSA
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Yuan X, Gong W, Hu B. Virtual indigo carmine dyeing: New artificial intelligence-based chromoendoscopy technique. Dig Endosc 2023; 35:e8-e10. [PMID: 36300847 DOI: 10.1111/den.14448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 09/30/2022] [Indexed: 01/17/2023]
Abstract
Watch a video of this article.
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Affiliation(s)
- Xianglei Yuan
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
| | - Wei Gong
- Department of Gastroenterology, Shenzhen Hospital, Southern Medical University, Shenzhen, China
| | - Bing Hu
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
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Yamamoto S, Kinugasa H, Hamada K, Tomiya M, Tanimoto T, Ohto A, Toda A, Takei D, Matsubara M, Suzuki S, Inoue K, Tanaka T, Hiraoka S, Okada H, Kawahara Y. The diagnostic ability to classify neoplasias occurring in inflammatory bowel disease by artificial intelligence and endoscopists: A pilot study. J Gastroenterol Hepatol 2022; 37:1610-1616. [PMID: 35644932 DOI: 10.1111/jgh.15904] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 05/18/2022] [Accepted: 05/24/2022] [Indexed: 01/08/2023]
Abstract
BACKGROUND AND AIM Although endoscopic resection with careful surveillance instead of total proctocolectomy become to be permitted for visible low-grade dysplasia, it is unclear how accurately endoscopists can differentiate these lesions, as classifying neoplasias occurring in inflammatory bowel disease (IBDN) is exceedingly challenging due to background chronic inflammation. We evaluated a pilot model of an artificial intelligence (AI) system for classifying IBDN and compared it with the endoscopist's ability. METHODS This study used a deep convolutional neural network, the EfficientNet-B3. Among patients who underwent treatment for IBDN at two hospitals between 2003 and 2021, we selected 862 non-magnified endoscopic images from 99 IBDN lesions and utilized 6 375 352 images that were increased by data augmentation for the development of AI. We evaluated the diagnostic ability of AI using two classifications: the "adenocarcinoma/high-grade dysplasia" and "low-grade dysplasia/sporadic adenoma/normal mucosa" groups. We compared the diagnostic accuracy between AI and endoscopists (three non-experts and four experts) using 186 test set images. RESULTS The diagnostic ability of the experts/non-experts/AI for the two classifications in the test set images had a sensitivity of 60.5% (95% confidence interval [CI]: 54.5-66.3)/70.5% (95% CI: 63.8-76.6)/72.5% (95% CI: 60.4-82.5), specificity of 88.0% (95% CI: 84.7-90.8)/78.8% (95% CI: 74.3-83.1)/82.9% (95% CI: 74.8-89.2), and accuracy of 77.8% (95% CI: 74.7-80.8)/75.8% (95% CI: 72-79.3)/79.0% (95% CI: 72.5-84.6), respectively. CONCLUSIONS The diagnostic accuracy of the two classifications of IBDN was higher than that of the experts. Our AI system is valuable enough to contribute to the next generation of clinical practice.
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Affiliation(s)
- Shumpei Yamamoto
- Department of Gastroenterology and Hepatology, Okayama University Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama, Japan.,Department of internal medicine, Japanese Red Cross Himeji Hospital, Himeji, Japan
| | - Hideaki Kinugasa
- Department of Gastroenterology and Hepatology, Okayama University Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama, Japan
| | - Kenta Hamada
- Department of Practical Gastrointestinal Endoscopy, Okayama University Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama, Japan
| | - Masahiro Tomiya
- Business Strategy Division, Ryobi Systems Co., Ltd., Okayama, Japan
| | | | - Akimitsu Ohto
- Business Strategy Division, Ryobi Systems Co., Ltd., Okayama, Japan
| | - Akira Toda
- Business Strategy Division, Ryobi Systems Co., Ltd., Okayama, Japan
| | - Daisuke Takei
- Department of Gastroenterology, Sumitomo Besshi Hospital, Niihama, Japan
| | - Minoru Matsubara
- Department of Gastroenterology, Sumitomo Besshi Hospital, Niihama, Japan
| | - Seiyu Suzuki
- Department of Gastroenterology, Sumitomo Besshi Hospital, Niihama, Japan
| | - Kosuke Inoue
- Department of Pathology, Sumitomo Besshi Hospital, Niihama, Japan
| | - Takehiro Tanaka
- Department of Pathology, Okayama University Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama, Japan
| | - Sakiko Hiraoka
- Department of Gastroenterology and Hepatology, Okayama University Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama, Japan
| | - Hiroyuki Okada
- Department of Gastroenterology and Hepatology, Okayama University Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama, Japan.,Department of internal medicine, Japanese Red Cross Himeji Hospital, Himeji, Japan
| | - Yoshiro Kawahara
- Department of Practical Gastrointestinal Endoscopy, Okayama University Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama, Japan
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Widya AR, Monno Y, Okutomi M, Suzuki S, Gotoda T, Miki K. Learning-Based Depth and Pose Estimation for Monocular Endoscope with Loss Generalization. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:3547-3552. [PMID: 34892005 DOI: 10.1109/embc46164.2021.9630156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Gastroendoscopy has been a clinical standard for diagnosing and treating conditions that affect a part of a patient's digestive system, such as the stomach. Despite the fact that gastroendoscopy has a lot of advantages for patients, there exist some challenges for practitioners, such as the lack of 3D perception, including the depth and the endoscope pose information. Such challenges make navigating the endoscope and localizing any found lesion in a digestive tract difficult. To tackle these problems, deep learning-based approaches have been proposed to provide monocular gastroendoscopy with additional yet important depth and pose information. In this paper, we propose a novel supervised approach to train depth and pose estimation networks using consecutive endoscopy images to assist the endoscope navigation in the stomach. We firstly generate real depth and pose training data using our previously proposed whole stomach 3D reconstruction pipeline to avoid poor generalization ability between computer-generated (CG) models and real data for the stomach. In addition, we propose a novel generalized photometric loss function to avoid the complicated process of finding proper weights for balancing the depth and the pose loss terms, which is required for existing direct depth and pose supervision approaches. We then experimentally show that our proposed generalized loss performs better than existing direct supervision losses.
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