1
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Guo X, Xu L, Li S, Xu M, Chu Y, Jiang Q. Cascade-EC Network: Recognition of Gastrointestinal Multiple Lesions Based on EfficientNet and CA_stm_Retinanet. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:1-11. [PMID: 38587768 DOI: 10.1007/s10278-024-01096-9] [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: 10/23/2023] [Revised: 03/17/2024] [Accepted: 03/20/2024] [Indexed: 04/09/2024]
Abstract
Capsule endoscopy (CE) is non-invasive and painless during gastrointestinal examination. However, capsule endoscopy can increase the workload of image reviewing for clinicians, making it prone to missed and misdiagnosed diagnoses. Current researches primarily concentrated on binary classifiers, multiple classifiers targeting fewer than four abnormality types and detectors within a specific segment of the digestive tract, and segmenters for a single type of anomaly. Due to intra-class variations, the task of creating a unified scheme for detecting multiple gastrointestinal diseases is particularly challenging. A cascade neural network designed in this study, Cascade-EC, can automatically identify and localize four types of gastrointestinal lesions in CE images: angiectasis, bleeding, erosion, and polyp. Cascade-EC consists of EfficientNet for image classification and CA_stm_Retinanet for lesion detection and location. As the first layer of Cascade-EC, the EfficientNet network classifies CE images. CA_stm_Retinanet, as the second layer, performs the target detection and location task on the classified image. CA_stm_Retinanet adopts the general architecture of Retinanet. Its feature extraction module is the CA_stm_Backbone from the stack of CA_stm Block. CA_stm Block adopts the split-transform-merge strategy and introduces the coordinate attention. The dataset in this study is from Shanghai East Hospital, collected by PillCam SB3 and AnKon capsule endoscopes, which contains a total of 7936 images of 317 patients from the years 2017 to 2021. In the testing set, the average precision of Cascade-EC in the multi-lesions classification task was 94.55%, the average recall was 90.60%, and the average F1 score was 92.26%. The mean mAP@ 0.5 of Cascade-EC for detecting the four types of diseases is 85.88%. The experimental results show that compared with a single target detection network, Cascade-EC has better performance and can effectively assist clinicians to classify and detect multiple lesions in CE images.
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Affiliation(s)
- Xudong Guo
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
| | - Lei Xu
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Shengnan Li
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Meidong Xu
- Endoscopy Center, Department of Gastroenterology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, 200120, China.
| | - Yuan Chu
- Endoscopy Center, Department of Gastroenterology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, 200120, China
| | - Qinfen Jiang
- Department of Information Management, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, 200120, China
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2
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Kafetzis I, Fuchs KH, Sodmann P, Troya J, Zoller W, Meining A, Hann A. Efficient artificial intelligence-based assessment of the gastroesophageal valve with Hill classification through active learning. Sci Rep 2024; 14:18825. [PMID: 39138220 PMCID: PMC11322637 DOI: 10.1038/s41598-024-68866-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 07/29/2024] [Indexed: 08/15/2024] Open
Abstract
Standardized assessment of the gastroesophageal valve during endoscopy, attainable via the Hill classification, is important for clinical assessment and therapeutic decision making. The Hill classification is associated with the presence of hiatal hernia (HH), a common endoscopic finding connected to gastro-esophageal reflux disease. A novel efficient medical artificial intelligence (AI) training pipeline using active learning (AL) is designed. We identified 21,970 gastroscopic images as training data and used our AL to train a model for predicting the Hill classification and detecting HH. Performance of the AL and traditionally trained models were evaluated on an external expert-annotated image collection. The AL model achieved accuracy of 76%. A traditionally trained model with 125% more training data achieved 77% accuracy. Furthermore, the AL model achieved higher precision than the traditional one for rare classes, with 0.54 versus 0.39 (p < 0.05) for grade 3 and 0.72 versus 0.61 (p < 0.05) for grade 4. In detecting HH, the AL model achieved 94% accuracy, 0.72 precision and 0.74 recall. Our AL pipeline is more efficient than traditional methods in training AI for endoscopy.
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Affiliation(s)
- Ioannis Kafetzis
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine 2, University Hospital Würzburg, Oberdürrbacherstr. 6, 97080, Würzburg, Germany.
| | - Karl-Hermann Fuchs
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine 2, University Hospital Würzburg, Oberdürrbacherstr. 6, 97080, Würzburg, Germany
| | - Philipp Sodmann
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine 2, University Hospital Würzburg, Oberdürrbacherstr. 6, 97080, Würzburg, Germany
| | - Joel Troya
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine 2, University Hospital Würzburg, Oberdürrbacherstr. 6, 97080, Würzburg, Germany
| | - Wolfram Zoller
- Clinic for General Internal Medicine, Gastroenterology, Hepatology and Infectiology, Pneumology, Klinikum Stuttgart-Katharinenhospital, Kriegsbergstr. 60, 70174, Stuttgart, Germany
| | - Alexander Meining
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine 2, University Hospital Würzburg, Oberdürrbacherstr. 6, 97080, Würzburg, Germany
| | - Alexander Hann
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine 2, University Hospital Würzburg, Oberdürrbacherstr. 6, 97080, Würzburg, Germany
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3
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Wan JJ, Zhu PC, Chen BL, Yu YT. A semantic feature enhanced YOLOv5-based network for polyp detection from colonoscopy images. Sci Rep 2024; 14:15478. [PMID: 38969765 PMCID: PMC11226707 DOI: 10.1038/s41598-024-66642-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 07/03/2024] [Indexed: 07/07/2024] Open
Abstract
Colorectal cancer (CRC) is a common digestive system tumor with high morbidity and mortality worldwide. At present, the use of computer-assisted colonoscopy technology to detect polyps is relatively mature, but it still faces some challenges, such as missed or false detection of polyps. Therefore, how to improve the detection rate of polyps more accurately is the key to colonoscopy. To solve this problem, this paper proposes an improved YOLOv5-based cancer polyp detection method for colorectal cancer. The method is designed with a new structure called P-C3 incorporated into the backbone and neck network of the model to enhance the expression of features. In addition, a contextual feature augmentation module was introduced to the bottom of the backbone network to increase the receptive field for multi-scale feature information and to focus on polyp features by coordinate attention mechanism. The experimental results show that compared with some traditional target detection algorithms, the model proposed in this paper has significant advantages for the detection accuracy of polyp, especially in the recall rate, which largely solves the problem of missed detection of polyps. This study will contribute to improve the polyp/adenoma detection rate of endoscopists in the process of colonoscopy, and also has important significance for the development of clinical work.
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Affiliation(s)
- Jing-Jing Wan
- Department of Gastroenterology, The Second People's Hospital of Huai'an, The Affiliated Huai'an Hospital of Xuzhou Medical University, Huaian, 223023, Jiangsu, China.
| | - Peng-Cheng Zhu
- Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, 223003, China.
| | - Bo-Lun Chen
- Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, 223003, China
| | - Yong-Tao Yu
- Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, 223003, China
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4
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Sahafi A, Koulaouzidis A, Lalinia M. Polypoid Lesion Segmentation Using YOLO-V8 Network in Wireless Video Capsule Endoscopy Images. Diagnostics (Basel) 2024; 14:474. [PMID: 38472946 DOI: 10.3390/diagnostics14050474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 01/26/2024] [Accepted: 02/19/2024] [Indexed: 03/14/2024] Open
Abstract
Gastrointestinal (GI) tract disorders are a significant public health issue. They are becoming more common and can cause serious health problems and high healthcare costs. Small bowel tumours (SBTs) and colorectal cancer (CRC) are both becoming more prevalent, especially among younger adults. Early detection and removal of polyps (precursors of malignancy) is essential for prevention. Wireless Capsule Endoscopy (WCE) is a procedure that utilises swallowable camera devices that capture images of the GI tract. Because WCE generates a large number of images, automated polyp segmentation is crucial. This paper reviews computer-aided approaches to polyp detection using WCE imagery and evaluates them using a dataset of labelled anomalies and findings. The study focuses on YOLO-V8, an improved deep learning model, for polyp segmentation and finds that it performs better than existing methods, achieving high precision and recall. The present study underscores the potential of automated detection systems in improving GI polyp identification.
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Affiliation(s)
- Ali Sahafi
- Department of Mechanical and Electrical Engineering, Digital and High-Frequency Electronics Section, University of Southern Denmark, 5230 Odense, Denmark
| | - Anastasios Koulaouzidis
- Surgical Research Unit, Odense University Hospital, 5000 Svendborg, Denmark
- Department of Clinical Research, University of Southern Denmark, 5230 Odense, Denmark
- Department of Medicine, OUH Svendborg Sygehus, 5700 Svendborg, Denmark
- Department of Social Medicine and Public Health, Pomeranian Medical University, 70204 Szczecin, Poland
| | - Mehrshad Lalinia
- Department of Mechanical and Electrical Engineering, Digital and High-Frequency Electronics Section, University of Southern Denmark, 5230 Odense, Denmark
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5
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Anari PY, Obiezu F, Lay N, Firouzabadi FD, Chaurasia A, Golagha M, Singh S, Homayounieh F, Zahergivar A, Harmon S, Turkbey E, Gautam R, Ma K, Merino M, Jones EC, Ball MW, Marston Linehan W, Turkbey B, Malayeri AA. Using YOLO v7 to Detect Kidney in Magnetic Resonance Imaging. ARXIV 2024:arXiv:2402.05817v2. [PMID: 38903734 PMCID: PMC11188135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 06/22/2024]
Abstract
Introduction This study explores the use of the latest You Only Look Once (YOLO V7) object detection method to enhance kidney detection in medical imaging by training and testing a modified YOLO V7 on medical image formats. Methods Study includes 878 patients with various subtypes of renal cell carcinoma (RCC) and 206 patients with normal kidneys. A total of 5657 MRI scans for 1084 patients were retrieved. 326 patients with 1034 tumors recruited from a retrospective maintained database, and bounding boxes were drawn around their tumors. A primary model was trained on 80% of annotated cases, with 20% saved for testing (primary test set). The best primary model was then used to identify tumors in the remaining 861 patients and bounding box coordinates were generated on their scans using the model. Ten benchmark training sets were created with generated coordinates on not-segmented patients. The final model used to predict the kidney in the primary test set. We reported the positive predictive value (PPV), sensitivity, and mean average precision (mAP). Results The primary training set showed an average PPV of 0.94 ± 0.01, sensitivity of 0.87 ± 0.04, and mAP of 0.91 ± 0.02. The best primary model yielded a PPV of 0.97, sensitivity of 0.92, and mAP of 0.95. The final model demonstrated an average PPV of 0.95 ± 0.03, sensitivity of 0.98 ± 0.004, and mAP of 0.95 ± 0.01. Conclusion Using a semi-supervised approach with a medical image library, we developed a high-performing model for kidney detection. Further external validation is required to assess the model's generalizability.
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Affiliation(s)
- Pouria Yazdian Anari
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, USA
| | - Fiona Obiezu
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, USA
| | - Nathan Lay
- Artificial Intelligence Resource, National Institutes of Health, USA
| | | | - Aditi Chaurasia
- Urology Oncology Branch, National cancer institutes, National Institutes of Health, USA
| | - Mahshid Golagha
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, USA
| | - Shiva Singh
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, USA
| | - Fatemeh Homayounieh
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, USA
| | - Aryan Zahergivar
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, USA
| | - Stephanie Harmon
- Artificial Intelligence Resource, National Institutes of Health, USA
| | - Evrim Turkbey
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, USA
| | - Rabindra Gautam
- Urology Oncology Branch, National cancer institutes, National Institutes of Health, USA
| | - Kevin Ma
- Artificial Intelligence Resource, National Institutes of Health, USA
| | - Maria Merino
- Pathology Department, National Cancer Institutes, National Institutes of Health, USA
| | - Elizabeth C Jones
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, USA
| | - Mark W. Ball
- Urology Oncology Branch, National cancer institutes, National Institutes of Health, USA
| | - W. Marston Linehan
- Urology Oncology Branch, National cancer institutes, National Institutes of Health, USA
| | - Baris Turkbey
- Artificial Intelligence Resource, National Institutes of Health, USA
| | - Ashkan A. Malayeri
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, USA
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6
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Zhu PC, Wan JJ, Shao W, Meng XC, Chen BL. Colorectal image analysis for polyp diagnosis. Front Comput Neurosci 2024; 18:1356447. [PMID: 38404511 PMCID: PMC10884282 DOI: 10.3389/fncom.2024.1356447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 01/05/2024] [Indexed: 02/27/2024] Open
Abstract
Colorectal polyp is an important early manifestation of colorectal cancer, which is significant for the prevention of colorectal cancer. Despite timely detection and manual intervention of colorectal polyps can reduce their chances of becoming cancerous, most existing methods ignore the uncertainties and location problems of polyps, causing a degradation in detection performance. To address these problems, in this paper, we propose a novel colorectal image analysis method for polyp diagnosis via PAM-Net. Specifically, a parallel attention module is designed to enhance the analysis of colorectal polyp images for improving the certainties of polyps. In addition, our method introduces the GWD loss to enhance the accuracy of polyp diagnosis from the perspective of polyp location. Extensive experimental results demonstrate the effectiveness of the proposed method compared with the SOTA baselines. This study enhances the performance of polyp detection accuracy and contributes to polyp detection in clinical medicine.
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Affiliation(s)
- Peng-Cheng Zhu
- Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, China
| | - Jing-Jing Wan
- Department of Gastroenterology, The Second People's Hospital of Huai'an, The Affiliated Huai'an Hospital of Xuzhou Medical University, Huaian, Jiangsu, China
| | - Wei Shao
- Nanjing University of Aeronautics and Astronautics Shenzhen Research Institute, Shenzhen, China
| | - Xian-Chun Meng
- Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, China
| | - Bo-Lun Chen
- Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, China
- Department of Physics, University of Fribourg, Fribourg, Switzerland
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7
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Kourounis G, Elmahmudi AA, Thomson B, Hunter J, Ugail H, Wilson C. Computer image analysis with artificial intelligence: a practical introduction to convolutional neural networks for medical professionals. Postgrad Med J 2023; 99:1287-1294. [PMID: 37794609 PMCID: PMC10658730 DOI: 10.1093/postmj/qgad095] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 09/06/2023] [Accepted: 09/13/2023] [Indexed: 10/06/2023]
Abstract
Artificial intelligence tools, particularly convolutional neural networks (CNNs), are transforming healthcare by enhancing predictive, diagnostic, and decision-making capabilities. This review provides an accessible and practical explanation of CNNs for clinicians and highlights their relevance in medical image analysis. CNNs have shown themselves to be exceptionally useful in computer vision, a field that enables machines to 'see' and interpret visual data. Understanding how these models work can help clinicians leverage their full potential, especially as artificial intelligence continues to evolve and integrate into healthcare. CNNs have already demonstrated their efficacy in diverse medical fields, including radiology, histopathology, and medical photography. In radiology, CNNs have been used to automate the assessment of conditions such as pneumonia, pulmonary embolism, and rectal cancer. In histopathology, CNNs have been used to assess and classify colorectal polyps, gastric epithelial tumours, as well as assist in the assessment of multiple malignancies. In medical photography, CNNs have been used to assess retinal diseases and skin conditions, and to detect gastric and colorectal polyps during endoscopic procedures. In surgical laparoscopy, they may provide intraoperative assistance to surgeons, helping interpret surgical anatomy and demonstrate safe dissection zones. The integration of CNNs into medical image analysis promises to enhance diagnostic accuracy, streamline workflow efficiency, and expand access to expert-level image analysis, contributing to the ultimate goal of delivering further improvements in patient and healthcare outcomes.
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Affiliation(s)
- Georgios Kourounis
- NIHR Blood and Transplant Research Unit, Newcastle University and Cambridge University, Newcastle upon Tyne, NE1 7RU, United Kingdom
- Institute of Transplantation, The Freeman Hospital, Newcastle upon Tyne, NE7 7DN, United Kingdom
| | - Ali Ahmed Elmahmudi
- Faculty of Engineering and Informatics, Bradford University, Bradford, BD7 1DP, United Kingdom
| | - Brian Thomson
- Faculty of Engineering and Informatics, Bradford University, Bradford, BD7 1DP, United Kingdom
| | - James Hunter
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, OX3 9DU, United Kingdom
| | - Hassan Ugail
- Faculty of Engineering and Informatics, Bradford University, Bradford, BD7 1DP, United Kingdom
| | - Colin Wilson
- NIHR Blood and Transplant Research Unit, Newcastle University and Cambridge University, Newcastle upon Tyne, NE1 7RU, United Kingdom
- Institute of Transplantation, The Freeman Hospital, Newcastle upon Tyne, NE7 7DN, United Kingdom
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8
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Yan T, Qin YY, Wong PK, Ren H, Wong CH, Yao L, Hu Y, Chan CI, Gao S, Chan PP. Semantic Segmentation of Gastric Polyps in Endoscopic Images Based on Convolutional Neural Networks and an Integrated Evaluation Approach. Bioengineering (Basel) 2023; 10:806. [PMID: 37508833 PMCID: PMC10376250 DOI: 10.3390/bioengineering10070806] [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: 06/13/2023] [Revised: 06/27/2023] [Accepted: 07/03/2023] [Indexed: 07/30/2023] Open
Abstract
Convolutional neural networks (CNNs) have received increased attention in endoscopic images due to their outstanding advantages. Clinically, some gastric polyps are related to gastric cancer, and accurate identification and timely removal are critical. CNN-based semantic segmentation can delineate each polyp region precisely, which is beneficial to endoscopists in the diagnosis and treatment of gastric polyps. At present, just a few studies have used CNN to automatically diagnose gastric polyps, and studies on their semantic segmentation are lacking. Therefore, we contribute pioneering research on gastric polyp segmentation in endoscopic images based on CNN. Seven classical semantic segmentation models, including U-Net, UNet++, DeepLabv3, DeepLabv3+, Pyramid Attention Network (PAN), LinkNet, and Muti-scale Attention Net (MA-Net), with the encoders of ResNet50, MobineNetV2, or EfficientNet-B1, are constructed and compared based on the collected dataset. The integrated evaluation approach to ascertaining the optimal CNN model combining both subjective considerations and objective information is proposed since the selection from several CNN models is difficult in a complex problem with conflicting multiple criteria. UNet++ with the MobineNet v2 encoder obtains the best scores in the proposed integrated evaluation method and is selected to build the automated polyp-segmentation system. This study discovered that the semantic segmentation model has a high clinical value in the diagnosis of gastric polyps, and the integrated evaluation approach can provide an impartial and objective tool for the selection of numerous models. Our study can further advance the development of endoscopic gastrointestinal disease identification techniques, and the proposed evaluation technique has implications for mathematical model-based selection methods for clinical technologies.
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Affiliation(s)
- Tao Yan
- School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang 441053, China
- Department of Electromechanical Engineering, University of Macau, Taipa, Macau 999078, China
- Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang 441021, China
| | - Ye Ying Qin
- Department of Electromechanical Engineering, University of Macau, Taipa, Macau 999078, China
| | - Pak Kin Wong
- Department of Electromechanical Engineering, University of Macau, Taipa, Macau 999078, China
| | - Hao Ren
- Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang 441021, China
| | - Chi Hong Wong
- Faculty of Medicine, Macau University of Science and Technology, Taipa, Macau 999078, China
| | - Liang Yao
- Department of Electromechanical Engineering, University of Macau, Taipa, Macau 999078, China
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Ying Hu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Cheok I Chan
- School of Medicine, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Shan Gao
- Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang 441021, China
| | - Pui Pun Chan
- Department of General Surgery, Centro Hospitalar Conde de São Januário, Macau 999078, China
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9
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Yoo BS, Houston KV, D'Souza SM, Elmahdi A, Davis I, Vilela A, Parekh PJ, Johnson DA. Advances and horizons for artificial intelligence of endoscopic screening and surveillance of gastric and esophageal disease. Artif Intell Med Imaging 2022; 3:70-86. [DOI: 10.35711/aimi.v3.i3.70] [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: 01/28/2022] [Revised: 05/18/2022] [Accepted: 06/20/2022] [Indexed: 02/06/2023] Open
Abstract
The development of artificial intelligence in endoscopic assessment of the gastrointestinal tract has shown progressive enhancement in diagnostic acuity. This review discusses the expanding applications for gastric and esophageal diseases. The gastric section covers the utility of AI in detecting and characterizing gastric polyps and further explores prevention, detection, and classification of gastric cancer. The esophageal discussion highlights applications for use in screening and surveillance in Barrett's esophagus and in high-risk conditions for esophageal squamous cell carcinoma. Additionally, these discussions highlight applications for use in assessing eosinophilic esophagitis and future potential in assessing esophageal microbiome changes.
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Affiliation(s)
- Byung Soo Yoo
- Department of Internal Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
| | - Kevin V Houston
- Department of Internal Medicine, Virginia Commonwealth University, Richmond, VA 23298, United States
| | - Steve M D'Souza
- Department of Internal Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
| | - Alsiddig Elmahdi
- Department of Internal Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
| | - Isaac Davis
- Department of Internal Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
| | - Ana Vilela
- Department of Internal Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
| | - Parth J Parekh
- Division of Gastroenterology, Department of Internal Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
| | - David A Johnson
- Division of Gastroenterology, Department of Internal Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
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10
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Sun Y, Li Y, Wang P, He D, Wang Z. Lesion Segmentation in Gastroscopic Images Using Generative Adversarial Networks. J Digit Imaging 2022; 35:459-468. [PMID: 35132523 PMCID: PMC9156598 DOI: 10.1007/s10278-022-00591-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 01/17/2022] [Accepted: 01/18/2022] [Indexed: 12/15/2022] Open
Abstract
The segmentation of the lesion region in gastroscopic images is highly important for the detection and treatment of early gastric cancer. This paper proposes a novel approach for gastric lesion segmentation by using generative adversarial training. First, a segmentation network is designed to generate accurate segmentation masks for gastric lesions. The proposed segmentation network adds residual blocks to the encoding and decoding path of U-Net. The cascaded dilated convolution is also added at the bottleneck of U-Net. The residual connection promotes information propagation, while dilated convolution integrates multi-scale context information. Meanwhile, a discriminator is used to distinguish the generated and real segmentation masks. The proposed discriminator is a Markov discriminator (Patch-GAN), which discriminates each [Formula: see text] matrix in the image. In the process of network training, the adversary training mechanism is used to iteratively optimize the generator and the discriminator until they converge at the same time. The experimental results show that the dice, accuracy, and recall are 86.6%, 91.9%, and 87.3%, respectively. These metrics are significantly better than the existing models, which proves the effectiveness of this method and can meet the needs of clinical diagnosis and treatment.
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Affiliation(s)
- Yaru Sun
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Yunqi Li
- Department of Gastroenterology, The First Medical Center, Chinese PLA General Hospital, Beijing, 100853, China
| | - Pengfei Wang
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Dongzhi He
- Faculty of Information Technology, Beijing University of Technology, Beijing, China.
| | - Zhiqiang Wang
- Department of Gastroenterology, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100853, China.
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11
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Amin J, Anjum MA, Sharif M, Kadry S, Nadeem A, Ahmad SF. Liver Tumor Localization Based on YOLOv3 and 3D-Semantic Segmentation Using Deep Neural Networks. Diagnostics (Basel) 2022; 12:diagnostics12040823. [PMID: 35453870 PMCID: PMC9025116 DOI: 10.3390/diagnostics12040823] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 03/18/2022] [Accepted: 03/22/2022] [Indexed: 12/17/2022] Open
Abstract
Worldwide, more than 1.5 million deaths are occur due to liver cancer every year. The use of computed tomography (CT) for early detection of liver cancer could save millions of lives per year. There is also an urgent need for a computerized method to interpret, detect and analyze CT scans reliably, easily, and correctly. However, precise segmentation of minute tumors is a difficult task because of variation in the shape, intensity, size, low contrast of the tumor, and the adjacent tissues of the liver. To address these concerns, a model comprised of three parts: synthetic image generation, localization, and segmentation, is proposed. An optimized generative adversarial network (GAN) is utilized for generation of synthetic images. The generated images are localized by using the improved localization model, in which deep features are extracted from pre-trained Resnet-50 models and fed into a YOLOv3 detector as an input. The proposed modified model localizes and classifies the minute liver tumor with 0.99 mean average precision (mAp). The third part is segmentation, in which pre-trained Inceptionresnetv2 employed as a base-Network of Deeplabv3 and subsequently is trained on fine-tuned parameters with annotated ground masks. The experiments reflect that the proposed approach has achieved greater than 95% accuracy in the testing phase and it is proven that, in comparison to the recently published work in this domain, this research has localized and segmented the liver and minute liver tumor with more accuracy.
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Affiliation(s)
- Javaria Amin
- Department of Computer Science, University of Wah, Wah Cantt 47040, Pakistan;
| | | | - Muhammad Sharif
- Department of Computer Science, Comsats University Islamabad, Wah Campus, Wah Cantt 47040, Pakistan;
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, 4609 Kristiansand, Norway
- Correspondence:
| | - Ahmed Nadeem
- Department of Pharmacology & Toxicology, College of Pharmacy, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia; (A.N.); (S.F.A.)
| | - Sheikh F. Ahmad
- Department of Pharmacology & Toxicology, College of Pharmacy, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia; (A.N.); (S.F.A.)
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Polyp Detection from Colorectum Images by Using Attentive YOLOv5. Diagnostics (Basel) 2021; 11:diagnostics11122264. [PMID: 34943501 PMCID: PMC8700704 DOI: 10.3390/diagnostics11122264] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Revised: 11/26/2021] [Accepted: 11/30/2021] [Indexed: 01/05/2023] Open
Abstract
Background: High-quality colonoscopy is essential to prevent the occurrence of colorectal cancers. The data of colonoscopy are mainly stored in the form of images. Therefore, artificial intelligence-assisted colonoscopy based on medical images is not only a research hotspot, but also one of the effective auxiliary means to improve the detection rate of adenomas. This research has become the focus of medical institutions and scientific research departments and has important clinical and scientific research value. Methods: In this paper, we propose a YOLOv5 model based on a self-attention mechanism for polyp target detection. This method uses the idea of regression, using the entire image as the input of the network and directly returning the target frame of this position in multiple positions of the image. In the feature extraction process, an attention mechanism is added to enhance the contribution of information-rich feature channels and weaken the interference of useless channels; Results: The experimental results show that the method can accurately identify polyp images, especially for the small polyps and the polyps with inconspicuous contrasts, and the detection speed is greatly improved compared with the comparison algorithm. Conclusions: This study will be of great help in reducing the missed diagnosis of clinicians during endoscopy and treatment, and it is also of great significance to the development of clinicians’ clinical work.
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Durak S, Bayram B, Bakırman T, Erkut M, Doğan M, Gürtürk M, Akpınar B. Deep neural network approaches for detecting gastric polyps in endoscopic images. Med Biol Eng Comput 2021; 59:1563-1574. [PMID: 34259974 DOI: 10.1007/s11517-021-02398-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 06/18/2021] [Indexed: 12/18/2022]
Abstract
Gastrointestinal endoscopy is the primary method used for the diagnosis and treatment of gastric polyps. The early detection and removal of polyps is vitally important in preventing cancer development. Many studies indicate that a high workload can contribute to misdiagnosing gastric polyps, even for experienced physicians. In this study, we aimed to establish a deep learning-based computer-aided diagnosis system for automatic gastric polyp detection. A private gastric polyp dataset was generated for this purpose consisting of 2195 endoscopic images and 3031 polyp labels. Retrospective gastrointestinal endoscopy data from the Karadeniz Technical University, Farabi Hospital, were used in the study. YOLOv4, CenterNet, EfficientNet, Cross Stage ResNext50-SPP, YOLOv3, YOLOv3-SPP, Single Shot Detection, and Faster Regional CNN deep learning models were implemented and assessed to determine the most efficient model for precancerous gastric polyp detection. The dataset was split 70% and 30% for training and testing all the implemented models. YOLOv4 was determined to be the most accurate model, with an 87.95% mean average precision. We also evaluated all the deep learning models using a public gastric polyp dataset as the test data. The results show that YOLOv4 has significant potential applicability in detecting gastric polyps and can be used effectively in gastrointestinal CAD systems. Gastric Polyp Detection Process using Deep Learning with Private Dataset.
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Affiliation(s)
- Serdar Durak
- Faculty of Medicine, Department of Gastroenterology, Karadeniz Technical University, Trabzon, Turkey
| | - Bülent Bayram
- Department of Geoinformatics, Yildiz Technical University, Istanbul, Turkey
| | - Tolga Bakırman
- Department of Geoinformatics, Yildiz Technical University, Istanbul, Turkey.
| | - Murat Erkut
- Faculty of Medicine, Department of Gastroenterology, Karadeniz Technical University, Trabzon, Turkey
| | - Metehan Doğan
- Department of Geoinformatics, Yildiz Technical University, Istanbul, Turkey
| | - Mert Gürtürk
- Department of Geoinformatics, Yildiz Technical University, Istanbul, Turkey
| | - Burak Akpınar
- Department of Geoinformatics, Yildiz Technical University, Istanbul, Turkey
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