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Maity R, Raja Sankari VM, U S, N A R, Salvador AL. Explainable AI based automated segmentation and multi-stage classification of gastroesophageal reflux using machine learning techniques. Biomed Phys Eng Express 2024; 10:045058. [PMID: 38901416 DOI: 10.1088/2057-1976/ad5a14] [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: 01/14/2024] [Accepted: 06/20/2024] [Indexed: 06/22/2024]
Abstract
Presently, close to two million patients globally succumb to gastrointestinal reflux diseases (GERD). Video endoscopy represents cutting-edge technology in medical imaging, facilitating the diagnosis of various gastrointestinal ailments including stomach ulcers, bleeding, and polyps. However, the abundance of images produced by medical video endoscopy necessitates significant time for doctors to analyze them thoroughly, posing a challenge for manual diagnosis. This challenge has spurred research into computer-aided techniques aimed at diagnosing the plethora of generated images swiftly and accurately. The novelty of the proposed methodology lies in the development of a system tailored for the diagnosis of gastrointestinal diseases. The proposed work used an object detection method called Yolov5 for identifying abnormal region of interest and Deep LabV3+ for segmentation of abnormal regions in GERD. Further, the features are extracted from the segmented image and given as an input to the seven different machine learning classifiers and custom deep neural network model for multi-stage classification of GERD. The DeepLabV3+ attains an excellent segmentation accuracy of 95.2% and an F1 score of 93.3%. The custom dense neural network obtained a classification accuracy of 90.5%. Among the seven different machine learning classifiers, support vector machine (SVM) outperformed with classification accuracy of 87% compared to all other class outperformed combination of object detection, deep learning-based segmentation and machine learning classification enables the timely identification and surveillance of problems associated with GERD for healthcare providers.
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Affiliation(s)
- Rudrani Maity
- Biomedical Engineering Department, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, 603203, Tamil Nadu, India
| | - V M Raja Sankari
- Biomedical Engineering Department, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, 603203, Tamil Nadu, India
| | - Snekhalatha U
- Biomedical Engineering Department, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, 603203, Tamil Nadu, India
- College of Engineering, Architecture and Fine Arts, Batangas State University, Batangas, Philippines
| | - Rajesh N A
- Department of Medical Gastroenterology, SRM Medical College, Hospital and Research centre, Kattankulathur, 603203, Tamil Nadu, India
| | - Anela L Salvador
- College of Engineering, Architecture and Fine Arts, Batangas State University, Batangas, Philippines
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2
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Boulogne LH, Lorenz J, Kienzle D, Schön R, Ludwig K, Lienhart R, Jégou S, Li G, Chen C, Wang Q, Shi D, Maniparambil M, Müller D, Mertes S, Schröter N, Hellmann F, Elia M, Dirks I, Bossa MN, Berenguer AD, Mukherjee T, Vandemeulebroucke J, Sahli H, Deligiannis N, Gonidakis P, Huynh ND, Razzak I, Bouadjenek R, Verdicchio M, Borrelli P, Aiello M, Meakin JA, Lemm A, Russ C, Ionasec R, Paragios N, van Ginneken B, Revel-Dubois MP. The STOIC2021 COVID-19 AI challenge: Applying reusable training methodologies to private data. Med Image Anal 2024; 97:103230. [PMID: 38875741 DOI: 10.1016/j.media.2024.103230] [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: 07/23/2023] [Revised: 01/11/2024] [Accepted: 06/03/2024] [Indexed: 06/16/2024]
Abstract
Challenges drive the state-of-the-art of automated medical image analysis. The quantity of public training data that they provide can limit the performance of their solutions. Public access to the training methodology for these solutions remains absent. This study implements the Type Three (T3) challenge format, which allows for training solutions on private data and guarantees reusable training methodologies. With T3, challenge organizers train a codebase provided by the participants on sequestered training data. T3 was implemented in the STOIC2021 challenge, with the goal of predicting from a computed tomography (CT) scan whether subjects had a severe COVID-19 infection, defined as intubation or death within one month. STOIC2021 consisted of a Qualification phase, where participants developed challenge solutions using 2000 publicly available CT scans, and a Final phase, where participants submitted their training methodologies with which solutions were trained on CT scans of 9724 subjects. The organizers successfully trained six of the eight Final phase submissions. The submitted codebases for training and running inference were released publicly. The winning solution obtained an area under the receiver operating characteristic curve for discerning between severe and non-severe COVID-19 of 0.815. The Final phase solutions of all finalists improved upon their Qualification phase solutions.
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Affiliation(s)
- Luuk H Boulogne
- Radboud university medical center, P.O. Box 9101, 6500HB Nijmegen, The Netherlands.
| | - Julian Lorenz
- University of Augsburg, Universitätsstraße 2, 86159 Augsburg, Germany.
| | - Daniel Kienzle
- University of Augsburg, Universitätsstraße 2, 86159 Augsburg, Germany
| | - Robin Schön
- University of Augsburg, Universitätsstraße 2, 86159 Augsburg, Germany
| | - Katja Ludwig
- University of Augsburg, Universitätsstraße 2, 86159 Augsburg, Germany
| | - Rainer Lienhart
- University of Augsburg, Universitätsstraße 2, 86159 Augsburg, Germany
| | | | - Guang Li
- Keya medical technology co. ltd, Floor 20, Building A, 1 Ronghua South Road, Yizhuang Economic Development Zone, Daxing District, Beijing, PR China.
| | - Cong Chen
- Keya medical technology co. ltd, Floor 20, Building A, 1 Ronghua South Road, Yizhuang Economic Development Zone, Daxing District, Beijing, PR China
| | - Qi Wang
- Keya medical technology co. ltd, Floor 20, Building A, 1 Ronghua South Road, Yizhuang Economic Development Zone, Daxing District, Beijing, PR China
| | - Derik Shi
- Keya medical technology co. ltd, Floor 20, Building A, 1 Ronghua South Road, Yizhuang Economic Development Zone, Daxing District, Beijing, PR China
| | - Mayug Maniparambil
- ML-Labs, Dublin City University, N210, Marconi building, Dublin City University, Glasnevin, Dublin 9, Ireland.
| | - Dominik Müller
- University of Augsburg, Universitätsstraße 2, 86159 Augsburg, Germany; Faculty of Applied Computer Science, University of Augsburg, Germany
| | - Silvan Mertes
- Faculty of Applied Computer Science, University of Augsburg, Germany
| | - Niklas Schröter
- Faculty of Applied Computer Science, University of Augsburg, Germany
| | - Fabio Hellmann
- Faculty of Applied Computer Science, University of Augsburg, Germany
| | - Miriam Elia
- Faculty of Applied Computer Science, University of Augsburg, Germany.
| | - Ine Dirks
- Vrije Universiteit Brussel, Department of Electronics and Informatics, Pleinlaan 2, 1050 Brussels, Belgium; imec, Kapeldreef 75, 3001 Leuven, Belgium.
| | - Matías Nicolás Bossa
- Vrije Universiteit Brussel, Department of Electronics and Informatics, Pleinlaan 2, 1050 Brussels, Belgium; imec, Kapeldreef 75, 3001 Leuven, Belgium
| | - Abel Díaz Berenguer
- Vrije Universiteit Brussel, Department of Electronics and Informatics, Pleinlaan 2, 1050 Brussels, Belgium; imec, Kapeldreef 75, 3001 Leuven, Belgium
| | - Tanmoy Mukherjee
- Vrije Universiteit Brussel, Department of Electronics and Informatics, Pleinlaan 2, 1050 Brussels, Belgium; imec, Kapeldreef 75, 3001 Leuven, Belgium
| | - Jef Vandemeulebroucke
- Vrije Universiteit Brussel, Department of Electronics and Informatics, Pleinlaan 2, 1050 Brussels, Belgium; imec, Kapeldreef 75, 3001 Leuven, Belgium
| | - Hichem Sahli
- Vrije Universiteit Brussel, Department of Electronics and Informatics, Pleinlaan 2, 1050 Brussels, Belgium; imec, Kapeldreef 75, 3001 Leuven, Belgium
| | - Nikos Deligiannis
- Vrije Universiteit Brussel, Department of Electronics and Informatics, Pleinlaan 2, 1050 Brussels, Belgium; imec, Kapeldreef 75, 3001 Leuven, Belgium
| | - Panagiotis Gonidakis
- Vrije Universiteit Brussel, Department of Electronics and Informatics, Pleinlaan 2, 1050 Brussels, Belgium; imec, Kapeldreef 75, 3001 Leuven, Belgium
| | | | - Imran Razzak
- University of New South Wales, Sydney, Australia.
| | | | | | | | | | - James A Meakin
- Radboud university medical center, P.O. Box 9101, 6500HB Nijmegen, The Netherlands
| | - Alexander Lemm
- Amazon Web Services, Marcel-Breuer-Str. 12, 80807 München, Germany
| | - Christoph Russ
- Amazon Web Services, Marcel-Breuer-Str. 12, 80807 München, Germany
| | - Razvan Ionasec
- Amazon Web Services, Marcel-Breuer-Str. 12, 80807 München, Germany
| | - Nikos Paragios
- Keya medical technology co. ltd, Floor 20, Building A, 1 Ronghua South Road, Yizhuang Economic Development Zone, Daxing District, Beijing, PR China; TheraPanacea, 75004, Paris, France
| | - Bram van Ginneken
- Radboud university medical center, P.O. Box 9101, 6500HB Nijmegen, The Netherlands
| | - Marie-Pierre Revel-Dubois
- Department of Radiology, Université de Paris, APHP, Hôpital Cochin, 27 rue du Fg Saint Jacques, 75014 Paris, France
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3
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Tran TK, Tran MC, Joseph A, Phan PA, Grau V, Farmery AD. A systematic review of machine learning models for management, prediction and classification of ARDS. Respir Res 2024; 25:232. [PMID: 38834976 DOI: 10.1186/s12931-024-02834-x] [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: 02/13/2024] [Accepted: 05/04/2024] [Indexed: 06/06/2024] Open
Abstract
AIM Acute respiratory distress syndrome or ARDS is an acute, severe form of respiratory failure characterised by poor oxygenation and bilateral pulmonary infiltrates. Advancements in signal processing and machine learning have led to promising solutions for classification, event detection and predictive models in the management of ARDS. METHOD In this review, we provide systematic description of different studies in the application of Machine Learning (ML) and artificial intelligence for management, prediction, and classification of ARDS. We searched the following databases: Google Scholar, PubMed, and EBSCO from 2009 to 2023. A total of 243 studies was screened, in which, 52 studies were included for review and analysis. We integrated knowledge of previous work providing the state of art and overview of explainable decision models in machine learning and have identified areas for future research. RESULTS Gradient boosting is the most common and successful method utilised in 12 (23.1%) of the studies. Due to limitation of data size available, neural network and its variation is used by only 8 (15.4%) studies. Whilst all studies used cross validating technique or separated database for validation, only 1 study validated the model with clinician input. Explainability methods were presented in 15 (28.8%) of studies with the most common method is feature importance which used 14 times. CONCLUSION For databases of 5000 or fewer samples, extreme gradient boosting has the highest probability of success. A large, multi-region, multi centre database is required to reduce bias and take advantage of neural network method. A framework for validating with and explaining ML model to clinicians involved in the management of ARDS would be very helpful for development and deployment of the ML model.
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Affiliation(s)
- Tu K Tran
- Department of Engineering and Science, University of Oxford, Oxford, UK.
- Nuffield Department of Clinical Neurosciences, Oxford Institute of Biomedical Engineering, University of Oxford, Oxford, UK.
| | - Minh C Tran
- Nuffield Division of Anaesthetics, University of Oxford, Oxford, UK
| | - Arun Joseph
- Nuffield Division of Anaesthetics, University of Oxford, Oxford, UK
| | - Phi A Phan
- Nuffield Division of Anaesthetics, University of Oxford, Oxford, UK
| | - Vicente Grau
- Department of Engineering and Science, University of Oxford, Oxford, UK
| | - Andrew D Farmery
- Nuffield Division of Anaesthetics, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, Oxford Institute of Biomedical Engineering, University of Oxford, Oxford, UK
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Daneshpajooh V, Ahmad D, Toth J, Bascom R, Higgins WE. Automatic lesion detection for narrow-band imaging bronchoscopy. J Med Imaging (Bellingham) 2024; 11:036002. [PMID: 38827776 PMCID: PMC11138083 DOI: 10.1117/1.jmi.11.3.036002] [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: 08/22/2023] [Revised: 04/04/2024] [Accepted: 05/14/2024] [Indexed: 06/05/2024] Open
Abstract
Purpose Early detection of cancer is crucial for lung cancer patients, as it determines disease prognosis. Lung cancer typically starts as bronchial lesions along the airway walls. Recent research has indicated that narrow-band imaging (NBI) bronchoscopy enables more effective bronchial lesion detection than other bronchoscopic modalities. Unfortunately, NBI video can be hard to interpret because physicians currently are forced to perform a time-consuming subjective visual search to detect bronchial lesions in a long airway-exam video. As a result, NBI bronchoscopy is not regularly used in practice. To alleviate this problem, we propose an automatic two-stage real-time method for bronchial lesion detection in NBI video and perform a first-of-its-kind pilot study of the method using NBI airway exam video collected at our institution. Approach Given a patient's NBI video, the first method stage entails a deep-learning-based object detection network coupled with a multiframe abnormality measure to locate candidate lesions on each video frame. The second method stage then draws upon a Siamese network and a Kalman filter to track candidate lesions over multiple frames to arrive at final lesion decisions. Results Tests drawing on 23 patient NBI airway exam videos indicate that the method can process an incoming video stream at a real-time frame rate, thereby making the method viable for real-time inspection during a live bronchoscopic airway exam. Furthermore, our studies showed a 93% sensitivity and 86% specificity for lesion detection; this compares favorably to a sensitivity and specificity of 80% and 84% achieved over a series of recent pooled clinical studies using the current time-consuming subjective clinical approach. Conclusion The method shows potential for robust lesion detection in NBI video at a real-time frame rate. Therefore, it could help enable more common use of NBI bronchoscopy for bronchial lesion detection.
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Affiliation(s)
- Vahid Daneshpajooh
- The Pennsylvania State University, School of Electrical Engineering and Computer Science, University Park, Pennsylvania, United States
| | - Danish Ahmad
- The Pennsylvania State University, College of Medicine, Hershey, Pennsylvania, United States
| | - Jennifer Toth
- The Pennsylvania State University, College of Medicine, Hershey, Pennsylvania, United States
| | - Rebecca Bascom
- The Pennsylvania State University, College of Medicine, Hershey, Pennsylvania, United States
| | - William E. Higgins
- The Pennsylvania State University, School of Electrical Engineering and Computer Science, University Park, Pennsylvania, United States
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5
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Ayana G, Barki H, Choe SW. Pathological Insights: Enhanced Vision Transformers for the Early Detection of Colorectal Cancer. Cancers (Basel) 2024; 16:1441. [PMID: 38611117 PMCID: PMC11010958 DOI: 10.3390/cancers16071441] [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: 03/13/2024] [Revised: 04/02/2024] [Accepted: 04/04/2024] [Indexed: 04/14/2024] Open
Abstract
Endoscopic pathological findings of the gastrointestinal tract are crucial for the early diagnosis of colorectal cancer (CRC). Previous deep learning works, aimed at improving CRC detection performance and reducing subjective analysis errors, are limited to polyp segmentation. Pathological findings were not considered and only convolutional neural networks (CNNs), which are not able to handle global image feature information, were utilized. This work introduces a novel vision transformer (ViT)-based approach for early CRC detection. The core components of the proposed approach are ViTCol, a boosted vision transformer for classifying endoscopic pathological findings, and PUTS, a vision transformer-based model for polyp segmentation. Results demonstrate the superiority of this vision transformer-based CRC detection method over existing CNN and vision transformer models. ViTCol exhibited an outstanding performance in classifying pathological findings, with an area under the receiver operating curve (AUC) value of 0.9999 ± 0.001 on the Kvasir dataset. PUTS provided outstanding results in segmenting polyp images, with mean intersection over union (mIoU) of 0.8673 and 0.9092 on the Kvasir-SEG and CVC-Clinic datasets, respectively. This work underscores the value of spatial transformers in localizing input images, which can seamlessly integrate into the main vision transformer network, enhancing the automated identification of critical image features for early CRC detection.
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Affiliation(s)
- Gelan Ayana
- Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39253, Republic of Korea;
- School of Biomedical Engineering, Jimma University, Jimma 378, Ethiopia
| | - Hika Barki
- Department of Artificial Intelligence Convergence, Pukyong National University, Busan 48513, Republic of Korea;
| | - Se-woon Choe
- Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39253, Republic of Korea;
- Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39253, Republic of Korea
- Emerging Pathogens Institute, University of Florida, Gainesville, FL 32608, USA
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6
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Zhang K, Wang H, Cheng Y, Liu H, Gong Q, Zeng Q, Zhang T, Wei G, Wei Z, Chen D. Early gastric cancer detection and lesion segmentation based on deep learning and gastroscopic images. Sci Rep 2024; 14:7847. [PMID: 38570595 PMCID: PMC10991264 DOI: 10.1038/s41598-024-58361-8] [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: 02/21/2023] [Accepted: 03/28/2024] [Indexed: 04/05/2024] Open
Abstract
Gastric cancer is a highly prevalent disease that poses a serious threat to public health. In clinical practice, gastroscopy is frequently used by medical practitioners to screen for gastric cancer. However, the symptoms of gastric cancer at different stages of advancement vary significantly, particularly in the case of early gastric cancer (EGC). The manifestations of EGC are often indistinct, leading to a detection rate of less than 10%. In recent years, researchers have focused on leveraging deep learning algorithms to assist medical professionals in detecting EGC and thereby improve detection rates. To enhance the ability of deep learning to detect EGC and segment lesions in gastroscopic images, an Improved Mask R-CNN (IMR-CNN) model was proposed. This model incorporates a "Bi-directional feature extraction and fusion module" and a "Purification module for feature channel and space" based on the Mask R-CNN (MR-CNN). Our study includes a dataset of 1120 images of EGC for training and validation of the models. The experimental results indicate that the IMR-CNN model outperforms the original MR-CNN model, with Precision, Recall, Accuracy, Specificity and F1-Score values of 92.9%, 95.3%, 93.9%, 92.5% and 94.1%, respectively. Therefore, our proposed IMR-CNN model has superior detection and lesion segmentation capabilities and can effectively aid doctors in diagnosing EGC from gastroscopic images.
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Affiliation(s)
- Kezhi Zhang
- Guangxi Key Laboratory of Information Functional Materials and Intelligent Information Processing, School of Physics and Electronics, Nanning Normal University, 175 Mingxiu East Road, Nanning, 530001, Guangxi, China
| | - Haibao Wang
- Guangxi Key Laboratory of Information Functional Materials and Intelligent Information Processing, School of Physics and Electronics, Nanning Normal University, 175 Mingxiu East Road, Nanning, 530001, Guangxi, China
| | - Yaru Cheng
- Department of Gastroenterology, Shandong Second Provincial General Hospital, 4 Duan Xing West Road, Jinan, 250022, Shandong, China
| | - Hongyan Liu
- Department of Gastroenterology, Shandong Second Provincial General Hospital, 4 Duan Xing West Road, Jinan, 250022, Shandong, China
| | - Qi Gong
- Department of Gastroenterology, Shandong Second Provincial General Hospital, 4 Duan Xing West Road, Jinan, 250022, Shandong, China
| | - Qian Zeng
- Guangxi Key Laboratory of Information Functional Materials and Intelligent Information Processing, School of Physics and Electronics, Nanning Normal University, 175 Mingxiu East Road, Nanning, 530001, Guangxi, China
| | - Tao Zhang
- Guangxi Key Laboratory of Information Functional Materials and Intelligent Information Processing, School of Physics and Electronics, Nanning Normal University, 175 Mingxiu East Road, Nanning, 530001, Guangxi, China
| | - Guoqiang Wei
- Guangxi Key Laboratory of Information Functional Materials and Intelligent Information Processing, School of Physics and Electronics, Nanning Normal University, 175 Mingxiu East Road, Nanning, 530001, Guangxi, China.
- School of Electronic Engineering, Hunan College of Information, Changsha, 410200, Hunan, China.
| | - Zhi Wei
- Department of Gastroenterology, Shandong Second Provincial General Hospital, 4 Duan Xing West Road, Jinan, 250022, Shandong, China.
| | - Dong Chen
- Guangxi Key Laboratory of Information Functional Materials and Intelligent Information Processing, School of Physics and Electronics, Nanning Normal University, 175 Mingxiu East Road, Nanning, 530001, Guangxi, China.
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Li G, Jin D, Zheng Y, Cui J, Gai W, Qi M. A generic plug & play diffusion-based denosing module for medical image segmentation. Neural Netw 2024; 172:106096. [PMID: 38194885 DOI: 10.1016/j.neunet.2024.106096] [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: 06/14/2023] [Revised: 12/11/2023] [Accepted: 12/31/2023] [Indexed: 01/11/2024]
Abstract
Medical image segmentation faces challenges because of the small sample size of the dataset and the fact that images often have noise and artifacts. In recent years, diffusion models have proven very effective in image generation and have been used widely in computer vision. This paper presents a new feature map denoising module (FMD) based on the diffusion model for feature refinement, which is plug-and-play, allowing flexible integration into popular used segmentation networks for seamless end-to-end training. We evaluate the performance of the FMD module on four models, UNet, UNeXt, TransUNet, and IB-TransUNet, by conducting experiments on four datasets. The experimental data analysis shows that adding the FMD module significantly positively impacts the model performance. Furthermore, especially for small lesion areas and minor organs, adding the FMD module allows users to obtain more accurate segmentation results than the original model.
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Affiliation(s)
- Guangju Li
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Dehu Jin
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Yuanjie Zheng
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Jia Cui
- School of Design, South China University of Technology, Guangzhou, China
| | - Wei Gai
- School of Software, Shandong University, Jinan, Shandong, China
| | - Meng Qi
- School of Information Science and Engineering, Shandong Normal University, Jinan, China.
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8
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Zhang Y, Yang G, Gong C, Zhang J, Wang S, Wang Y. Polyp segmentation with interference filtering and dynamic uncertainty mining. Phys Med Biol 2024; 69:075016. [PMID: 38382099 DOI: 10.1088/1361-6560/ad2b94] [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: 09/12/2023] [Accepted: 02/21/2024] [Indexed: 02/23/2024]
Abstract
Objective.Accurate polyp segmentation from colo-noscopy images plays a crucial role in the early diagnosis and treatment of colorectal cancer. However, existing polyp segmentation methods are inevitably affected by various image noises, such as reflections, motion blur, and feces, which significantly affect the performance and generalization of the model. In addition, coupled with ambiguous boundaries between polyps and surrounding tissue, i.e. small inter-class differences, accurate polyp segmentation remains a challenging problem.Approach.To address these issues, we propose a novel two-stage polyp segmentation method that leverages a preprocessing sub-network (Pre-Net) and a dynamic uncertainty mining network (DUMNet) to improve the accuracy of polyp segmentation. Pre-Net identifies and filters out interference regions before feeding the colonoscopy images to the polyp segmentation network DUMNet. Considering the confusing polyp boundaries, DUMNet employs the uncertainty mining module (UMM) to dynamically focus on foreground, background, and uncertain regions based on different pixel confidences. UMM helps to mine and enhance more detailed context, leading to coarse-to-fine polyp segmentation and precise localization of polyp regions.Main results.We conduct experiments on five popular polyp segmentation benchmarks: ETIS, CVC-ClinicDB, CVC-ColonDB, EndoScene, and Kvasir. Our method achieves state-of-the-art performance. Furthermore, the proposed Pre-Net has strong portability and can improve the accuracy of existing polyp segmentation models.Significance.The proposed method improves polyp segmentation performance by eliminating interference and mining uncertain regions. This aids doctors in making precise and reduces the risk of colorectal cancer. Our code will be released athttps://github.com/zyh5119232/DUMNet.
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Affiliation(s)
- Yunhua Zhang
- Northeastern University, Shenyang 110819, People's Republic of China
- DUT Artificial Intelligence Institute, Dalian 116024, People's Republic of China
| | - Gang Yang
- Northeastern University, Shenyang 110819, People's Republic of China
| | - Congjin Gong
- Northeastern University, Shenyang 110819, People's Republic of China
| | - Jianhao Zhang
- Northeastern University, Shenyang 110819, People's Republic of China
| | - Shuo Wang
- Northeastern University, Shenyang 110819, People's Republic of China
| | - Yutao Wang
- Northeastern University, Shenyang 110819, People's Republic of China
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9
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Gangrade S, Sharma PC, Sharma AK, Singh YP. Modified DeeplabV3+ with multi-level context attention mechanism for colonoscopy polyp segmentation. Comput Biol Med 2024; 170:108096. [PMID: 38320340 DOI: 10.1016/j.compbiomed.2024.108096] [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: 09/11/2023] [Revised: 01/31/2024] [Accepted: 02/01/2024] [Indexed: 02/08/2024]
Abstract
The development of automated methods for analyzing medical images of colon cancer is one of the main research fields. A colonoscopy is a medical treatment that enables a doctor to look for any abnormalities like polyps, cancer, or inflammatory tissue inside the colon and rectum. It falls under the category of gastrointestinal illnesses, and it claims the lives of almost two million people worldwide. Video endoscopy is an advanced medical imaging approach to diagnose gastrointestinal disorders such as inflammatory bowel, ulcerative colitis, esophagitis, and polyps. Medical video endoscopy generates several images, which must be reviewed by specialists. The difficulty of manual diagnosis has sparked research towards computer-aided techniques that can quickly and reliably diagnose all generated images. The proposed methodology establishes a framework for diagnosing coloscopy diseases. Endoscopists can lower the risk of polyps turning into cancer during colonoscopies by using more accurate computer-assisted polyp detection and segmentation. With the aim of creating a model that can automatically distinguish polyps from images, we presented a modified DeeplabV3+ model in this study to carry out segmentation tasks successfully and efficiently. The framework's encoder uses a pre-trained dilated convolutional residual network for optimal feature map resolution. The robustness of the modified model is tested against state-of-the-art segmentation approaches. In this work, we employed two publicly available datasets, CVC-Clinic DB and Kvasir-SEG, and obtained Dice similarity coefficients of 0.97 and 0.95, respectively. The results show that the improved DeeplabV3+ model improves segmentation efficiency and effectiveness in both software and hardware with only minor changes.
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Affiliation(s)
- Shweta Gangrade
- School of Information Technology, Manipal University Jaipur, Jaipur, Rajasthan, India; School of Computer Science and Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India
| | - Prakash Chandra Sharma
- School of Information Technology, Manipal University Jaipur, Jaipur, Rajasthan, India; School of Computer Science and Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India
| | - Akhilesh Kumar Sharma
- School of Information Technology, Manipal University Jaipur, Jaipur, Rajasthan, India; School of Computer Science and Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India
| | - Yadvendra Pratap Singh
- School of Information Technology, Manipal University Jaipur, Jaipur, Rajasthan, India; School of Computer Science and Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India.
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10
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Ali S, Ghatwary N, Jha D, Isik-Polat E, Polat G, Yang C, Li W, Galdran A, Ballester MÁG, Thambawita V, Hicks S, Poudel S, Lee SW, Jin Z, Gan T, Yu C, Yan J, Yeo D, Lee H, Tomar NK, Haithami M, Ahmed A, Riegler MA, Daul C, Halvorsen P, Rittscher J, Salem OE, Lamarque D, Cannizzaro R, Realdon S, de Lange T, East JE. Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge. Sci Rep 2024; 14:2032. [PMID: 38263232 PMCID: PMC10805888 DOI: 10.1038/s41598-024-52063-x] [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: 07/09/2023] [Accepted: 01/12/2024] [Indexed: 01/25/2024] Open
Abstract
Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-dependent procedures and occur in a highly complex organ topology. There exists a high missed detection rate and incomplete removal of colonic polyps. To assist in clinical procedures and reduce missed rates, automated methods for detecting and segmenting polyps using machine learning have been achieved in past years. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets from different centres, populations, modalities, and acquisition systems. To test this hypothesis rigorously, we, together with expert gastroenterologists, curated a multi-centre and multi-population dataset acquired from six different colonoscopy systems and challenged the computational expert teams to develop robust automated detection and segmentation methods in a crowd-sourcing Endoscopic computer vision challenge. This work put forward rigorous generalisability tests and assesses the usability of devised deep learning methods in dynamic and actual clinical colonoscopy procedures. We analyse the results of four top performing teams for the detection task and five top performing teams for the segmentation task. Our analyses demonstrate that the top-ranking teams concentrated mainly on accuracy over the real-time performance required for clinical applicability. We further dissect the devised methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets and routine clinical procedures.
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Affiliation(s)
- Sharib Ali
- School of Computing, Faculty of Engineering and Physical Sciences, University of Leeds, Leeds, LS2 9JT, UK.
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, OX3 7DQ, UK.
- Oxford National Institute for Health Research Biomedical Research Centre, Oxford, OX4 2PG, UK.
| | - Noha Ghatwary
- Computer Engineering Department, Arab Academy for Science and Technology, Smart Village, Giza, Egypt
| | - Debesh Jha
- SimulaMet, 0167, Oslo, Norway
- Department of Computer Science, UiT The Arctic University of Norway, Hansine Hansens veg 18, 9019, Tromsø, Norway
| | - Ece Isik-Polat
- Graduate School of Informatics, Middle East Technical University, 06800, Ankara, Turkey
| | - Gorkem Polat
- Graduate School of Informatics, Middle East Technical University, 06800, Ankara, Turkey
| | - Chen Yang
- City University of Hong Kong, Kowloon, Hong Kong
| | - Wuyang Li
- City University of Hong Kong, Kowloon, Hong Kong
| | - Adrian Galdran
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08018, Barcelona, Spain
| | - Miguel-Ángel González Ballester
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08018, Barcelona, Spain
- ICREA, Barcelona, Spain
| | | | | | - Sahadev Poudel
- Department of IT Convergence Engineering, Gachon University, Seongnam, 13120, Republic of Korea
| | - Sang-Woong Lee
- Department of IT Convergence Engineering, Gachon University, Seongnam, 13120, Republic of Korea
| | - Ziyi Jin
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China
| | - Tianyuan Gan
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China
| | - ChengHui Yu
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China
| | - JiangPeng Yan
- Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Doyeob Yeo
- Smart Sensing and Diagnosis Research Division, Korea Atomic Energy Research Institute, Taejon, 34057, Republic of Korea
| | - Hyunseok Lee
- Daegu-Gyeongbuk Medical Innovation Foundation, Medical Device Development Center, Taegu, 427724, Republic of Korea
| | - Nikhil Kumar Tomar
- NepAL Applied Mathematics and Informatics Institute for Research (NAAMII), Kathmandu, Nepal
| | - Mahmood Haithami
- Computer Science Department, University of Nottingham, Malaysia Campus, 43500, Semenyih, Malaysia
| | - Amr Ahmed
- Computer Science, Edge Hill University, Lancashire, United Kingdom
| | - Michael A Riegler
- SimulaMet, 0167, Oslo, Norway
- Department of Computer Science, UiT The Arctic University of Norway, Hansine Hansens veg 18, 9019, Tromsø, Norway
| | - Christian Daul
- CRAN UMR 7039, Université de Lorraine and CNRS, 54500, Vandœuvre-Lès-Nancy, France
| | - Pål Halvorsen
- SimulaMet, 0167, Oslo, Norway
- Oslo Metropolitan University, Pilestredet 46, 0167, Oslo, Norway
| | - Jens Rittscher
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, OX3 7DQ, UK
| | - Osama E Salem
- Faculty of Medicine, University of Alexandria, Alexandria, 21131, Egypt
| | - Dominique Lamarque
- Université de Versailles St-Quentin en Yvelines, Hôpital Ambroise Paré, 9 Av. Charles de Gaulle, 92100, Boulogne-Billancourt, France
| | - Renato Cannizzaro
- CRO Centro Riferimento Oncologico IRCCS Aviano Italy, Via Franco Gallini, 2, 33081, Aviano, PN, Italy
| | - Stefano Realdon
- CRO Centro Riferimento Oncologico IRCCS Aviano Italy, Via Franco Gallini, 2, 33081, Aviano, PN, Italy
- Veneto Institute of Oncology IOV-IRCCS, Via Gattamelata, 64, 35128, Padua, Italy
| | - Thomas de Lange
- Medical Department, Sahlgrenska University Hospital-Mölndal, Blå stråket 5, 413 45, Göteborg, Sweden
- Department of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, 41345, Göteborg, Sweden
- Augere Medical, Nedre Vaskegang 6, Oslo, 0186, Norway
| | - James E East
- Oxford National Institute for Health Research Biomedical Research Centre, Oxford, OX4 2PG, UK
- Translational Gastroenterology Unit, Nuffield Department of Medicine, Experimental Medicine Division, John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DU, UK
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11
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Wang G, Bai L, Wu Y, Chen T, Ren H. Rethinking exemplars for continual semantic segmentation in endoscopy scenes: Entropy-based mini-batch pseudo-replay. Comput Biol Med 2023; 165:107412. [PMID: 37696180 DOI: 10.1016/j.compbiomed.2023.107412] [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: 04/04/2023] [Revised: 08/05/2023] [Accepted: 08/27/2023] [Indexed: 09/13/2023]
Abstract
Endoscopy is a widely used technique for the early detection of diseases or robotic-assisted minimally invasive surgery (RMIS). Numerous deep learning (DL)-based research works have been developed for automated diagnosis or processing of endoscopic view. However, existing DL models may suffer from catastrophic forgetting. When new target classes are introduced over time or cross institutions, the performance of old classes may suffer severe degradation. More seriously, data privacy and storage issues may lead to the unavailability of old data when updating the model. Therefore, it is necessary to develop a continual learning (CL) methodology to solve the problem of catastrophic forgetting in endoscopic image segmentation. To tackle this, we propose a Endoscopy Continual Semantic Segmentation (EndoCSS) framework that does not involve the storage and privacy issues of exemplar data. The framework includes a mini-batch pseudo-replay (MB-PR) mechanism and a self-adaptive noisy cross-entropy (SAN-CE) loss. The MB-PR strategy circumvents privacy and storage issues by generating pseudo-replay images through a generative model. Meanwhile, the MB-PR strategy can also correct the model deviation to the replay data and current training data, which is aroused by the significant difference in the amount of current and replay images. Therefore, the model can perform effective representation learning on both new and old tasks. SAN-CE loss can help model fitting by adjusting the model's output logits, and also improve the robustness of training. Extensive continual semantic segmentation (CSS) experiments on public datasets demonstrate that our method can robustly and effectively address the catastrophic forgetting brought by class increment in endoscopy scenes. The results show that our framework holds excellent potential for real-world deployment in a streaming learning manner.
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Affiliation(s)
- Guankun Wang
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China.
| | - Long Bai
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China.
| | - Yanan Wu
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
| | - Tong Chen
- School of Electrical and Information Engineering, The University of Sydney, Sydney, Australia.
| | - Hongliang Ren
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China; Department of Biomedical Engineering, National University of Singapore, Singapore; Suzhou Research Institute, National University of Singapore, Suzhou, China; Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong, Hong Kong, China; Shenzhen Research Institute, The Chinese University of, Hong Kong, Shenzhen, China.
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12
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Hong Y, Yu Q, Mo F, Yin M, Xu C, Zhu S, Lin J, Xu G, Gao J, Liu L, Wang Y. Deep learning to predict esophageal variceal bleeding based on endoscopic images. J Int Med Res 2023; 51:3000605231200371. [PMID: 37818651 PMCID: PMC10566287 DOI: 10.1177/03000605231200371] [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: 05/23/2023] [Accepted: 08/24/2023] [Indexed: 10/12/2023] Open
Abstract
OBJECTIVE Esophageal varix (EV) bleeding is a particularly serious complications of cirrhosis. Prediction of EV bleeding requires extensive endoscopy experience; it remains unreliable and inefficient. This retrospective cohort study evaluated the feasibility of using deep learning (DL) to predict the 12-month risk of EV bleeding based on endoscopic images. METHODS Six DL models were trained to perform binary classification of endoscopic images of EV bleeding. The models were subsequently validated using an external test dataset, then compared with classifications performed by two endoscopists. RESULTS In the validation dataset, EfficientNet had the highest accuracy (0.910), followed by ConvMixer (0.898) and Xception (0.875). In the test dataset, EfficientNet maintained the highest accuracy (0.893), which was better than the endoscopists (0.800 and 0.763). Notably, one endoscopist displayed higher recall (0.905), compared with EfficientNet (0.870). When their predictions were assisted by artificial intelligence, the accuracies of the two endoscopists increased by 17.3% and 19.0%. Moreover, statistical agreement among the models was dependent on model architecture. CONCLUSIONS This study demonstrated the feasibility of using DL to predict the 12-month risk of EV bleeding based on endoscopic images. The findings suggest that artificial intelligence-aided diagnosis will be a useful addition to cirrhosis management.
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Affiliation(s)
- Yu Hong
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Qianqian Yu
- Department of Oncology, Jintan Affiliated Hospital of Jiangsu University, Jintan, China
| | - Feng Mo
- Department of General Surgery, Jintan Affiliated Hospital of Jiangsu University, Jintan, China
| | - Minyue Yin
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Chang Xu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Shiqi Zhu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Jiaxi Lin
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Guoting Xu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Jingwen Gao
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Lu Liu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Yu Wang
- Department of General Surgery, Jintan Affiliated Hospital of Jiangsu University, Jintan, China
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13
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Polat G, Kani HT, Ergenc I, Ozen Alahdab Y, Temizel A, Atug O. Improving the Computer-Aided Estimation of Ulcerative Colitis Severity According to Mayo Endoscopic Score by Using Regression-Based Deep Learning. Inflamm Bowel Dis 2023; 29:1431-1439. [PMID: 36382800 DOI: 10.1093/ibd/izac226] [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: 06/06/2022] [Indexed: 11/18/2022]
Abstract
BACKGROUND Assessment of endoscopic activity in ulcerative colitis (UC) is important for treatment decisions and monitoring disease progress. However, substantial inter- and intraobserver variability in grading impairs the assessment. Our aim was to develop a computer-aided diagnosis system using deep learning to reduce subjectivity and improve the reliability of the assessment. METHODS The cohort comprises 11 276 images from 564 patients who underwent colonoscopy for UC. We propose a regression-based deep learning approach for the endoscopic evaluation of UC according to the Mayo endoscopic score (MES). Five state-of-the-art convolutional neural network (CNN) architectures were used for the performance measurements and comparisons. Ten-fold cross-validation was used to train the models and objectively benchmark them. Model performances were assessed using quadratic weighted kappa and macro F1 scores for full Mayo score classification and kappa statistics and F1 score for remission classification. RESULTS Five classification-based CNNs used in the study were in excellent agreement with the expert annotations for all Mayo subscores and remission classification according to the kappa statistics. When the proposed regression-based approach was used, (1) the performance of most of the models statistically significantly increased and (2) the same model trained on different cross-validation folds produced more robust results on the test set in terms of deviation between different folds. CONCLUSIONS Comprehensive experimental evaluations show that commonly used classification-based CNN architectures have successful performance in evaluating endoscopic disease activity of UC. Integration of domain knowledge into these architectures further increases performance and robustness, accelerating their translation into clinical use.
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Affiliation(s)
- Gorkem Polat
- Graduate School of Informatics, Middle East Technical University, Ankara, Turkey
- Neuroscience and Neurotechnology Center of Excellence, Middle East Technical University, Ankara, Turkey
| | - Haluk Tarik Kani
- Department of Gastroenterology, School of Medicine, Marmara University, Istanbul, Turkey
| | - Ilkay Ergenc
- Department of Gastroenterology, School of Medicine, Marmara University, Istanbul, Turkey
| | - Yesim Ozen Alahdab
- Department of Gastroenterology, School of Medicine, Marmara University, Istanbul, Turkey
| | - Alptekin Temizel
- Graduate School of Informatics, Middle East Technical University, Ankara, Turkey
- Neuroscience and Neurotechnology Center of Excellence, Middle East Technical University, Ankara, Turkey
| | - Ozlen Atug
- Department of Gastroenterology, School of Medicine, Marmara University, Istanbul, Turkey
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14
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Gunasekaran H, Ramalakshmi K, Swaminathan DK, J A, Mazzara M. GIT-Net: An Ensemble Deep Learning-Based GI Tract Classification of Endoscopic Images. Bioengineering (Basel) 2023; 10:809. [PMID: 37508836 PMCID: PMC10376874 DOI: 10.3390/bioengineering10070809] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 06/14/2023] [Accepted: 07/02/2023] [Indexed: 07/30/2023] Open
Abstract
This paper presents an ensemble of pre-trained models for the accurate classification of endoscopic images associated with Gastrointestinal (GI) diseases and illnesses. In this paper, we propose a weighted average ensemble model called GIT-NET to classify GI-tract diseases. We evaluated the model on a KVASIR v2 dataset with eight classes. When individual models are used for classification, they are often prone to misclassification since they may not be able to learn the characteristics of all the classes adequately. This is due to the fact that each model may learn the characteristics of specific classes more efficiently than the other classes. We propose an ensemble model that leverages the predictions of three pre-trained models, DenseNet201, InceptionV3, and ResNet50 with accuracies of 94.54%, 88.38%, and 90.58%, respectively. The predictions of the base learners are combined using two methods: model averaging and weighted averaging. The performances of the models are evaluated, and the model averaging ensemble has an accuracy of 92.96% whereas the weighted average ensemble has an accuracy of 95.00%. The weighted average ensemble outperforms the model average ensemble and all individual models. The results from the evaluation demonstrate that utilizing an ensemble of base learners can successfully classify features that were incorrectly learned by individual base learners.
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Affiliation(s)
- Hemalatha Gunasekaran
- Information Technology, University of Technology and Applied Sciences, Ibri 516, Oman
| | - Krishnamoorthi Ramalakshmi
- Information Technology, Alliance College of Engineering and Design, Alliance University, Bengaluru 562106, India
| | | | - Andrew J
- Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
| | - Manuel Mazzara
- Institute of Software Development and Engineering, Innopolis University, 420500 Innopolis, Russia
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15
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Du W, Rao N, Yong J, Adjei PE, Hu X, Wang X, Gan T, Zhu L, Zeng B, Liu M, Xu Y. Early gastric cancer segmentation in gastroscopic images using a co-spatial attention and channel attention based triple-branch ResUnet. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 231:107397. [PMID: 36753915 DOI: 10.1016/j.cmpb.2023.107397] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 12/15/2022] [Accepted: 02/01/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE The artificial segmentation of early gastric cancer (EGC) lesions in gastroscopic images remains a challenging task due to reasons including the diversity of mucosal features, irregular edges of EGC lesions and nuances between EGC lesions and healthy background mucosa. Hence, this study proposed an automatic segmentation framework: co-spatial attention and channel attention based triple-branch ResUnet (CSA-CA-TB-ResUnet) to achieve accurate segmentation of EGC lesions for aiding clinical diagnosis and treatment. METHODS The input gastroscopic image sequences of the triple-branch segmentation network CSA-CA-TB-ResUnet is firstly generated by the designed multi-branch input preprocessing (MBIP) module in order to fully utilize massive correlation information among multiple gastroscopic images of the same a lesion. Then, the proposed CSA-CA-TB-ResUnet performs the segmentation of EGC lesion, in which the co-spatial attention (CSA) mechanism is designed to activate the spatial location of EGC lesions by leveraging on the correlations among multiple gastroscopic images of the same EGC lesion, and the channel attention (CA) mechanism is introduced to extract subtle discriminative features of EGC lesions by capturing the interdependencies between channel features. Finally, two gastroscopic images datasets from different digestive endoscopic centers in the southwest and northeast regions of China respectively were collected to validate the performances of proposed segmentation method. RESULTS The correlation information among gastroscopic images was confirmed to be able to improve the accuracy of EGC segmentation. On another unseen dataset, our EGC segmentation method achieves Jaccard similarity index (JSI) of 84.54% (95% confidence interval (CI), 83.49%-85.56%), threshold Jaccard index (TJI) of 81.73% (95% CI, 79.70%-83.61%), Dice similarity coefficient (DSC) of 91.08% (95% CI, 90.40%-91.76%) and pixel-wise accuracy (PA) of 91.18% (95% CI, 90.43%-91.87%), which is superior to other state-of-the-art methods. Even on the challenging small lesions, the segmentation results of our CSA-CA-TB-ResUnet-based method are consistently and significantly better than other state-of-the-art methods. We also compared the segmentation result of our model with the diagnostic accuracy with junior/senior expert. The comparison results indicated that our model performed better than the junior expert. CONCLUSIONS This study proposed a novel CSA-CA-TB-ResUnet-based EGC segmentation method and it has a potential for real-time application in improving EGC clinical diagnosis and minimally invasive surgery.
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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.
| | - Jiahao Yong
- 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
| | - Prince Ebenezer Adjei
- 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
| | - Xiaoming 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
| | - Xiaotong 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
| | - Tao Gan
- Digestive Endoscopic Center of West China Hospital, Sichuan University, Chengdu 610017, 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
| | - Mengyuan Liu
- The First Hospital of China Medical University, China Medical University, Shenyang 110001, China
| | - Yongxue Xu
- 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
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16
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Tang S, Yu X, Cheang CF, Ji X, Yu HH, Choi IC. CLELNet: A continual learning network for esophageal lesion analysis on endoscopic images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 231:107399. [PMID: 36780717 DOI: 10.1016/j.cmpb.2023.107399] [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: 11/02/2021] [Revised: 01/03/2023] [Accepted: 02/01/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE A deep learning-based intelligent diagnosis system can significantly reduce the burden of endoscopists in the daily analysis of esophageal lesions. Considering the need to add new tasks in the diagnosis system, a deep learning model that can train a series of tasks incrementally using endoscopic images is essential for identifying the types and regions of esophageal lesions. METHOD In this paper, we proposed a continual learning-based esophageal lesion network (CLELNet), in which a convolutional autoencoder was designed to extract representation features of endoscopic images among different esophageal lesions. The proposed CLELNet consists of shared layers and task-specific layers. Shared layers are used to extract common features among different lesions while task-specific layers can complete different tasks. The first two tasks trained by the CLELNet are the classification (task 1) and the segmentation (task 2). We collected a dataset of esophageal endoscopic images from Macau Kiang Wu Hospital for training and testing the CLELNet. RESULTS The experimental results showed that the classification accuracy of task 1 was 95.96%, and the Intersection Over Union and the Dice Similarity Coefficient of task 2 were 65.66% and 78.08%, respectively. CONCLUSIONS The proposed CLELNet can realize task-incremental learning without forgetting the previous tasks and thus become a useful computer-aided diagnosis system in esophageal lesions analysis.
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Affiliation(s)
- Suigu Tang
- Faculty of Innovation Engineering-School of Computer Science and Engineering, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau SAR
| | - Xiaoyuan Yu
- Faculty of Innovation Engineering-School of Computer Science and Engineering, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau SAR
| | - Chak Fong Cheang
- Faculty of Innovation Engineering-School of Computer Science and Engineering, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau SAR.
| | - Xiaoyu Ji
- Faculty of Innovation Engineering-School of Computer Science and Engineering, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau SAR
| | - Hon Ho Yu
- Kiang Wu Hospital, Rua de Coelho do Amaral, Macau SAR
| | - I Cheong Choi
- Kiang Wu Hospital, Rua de Coelho do Amaral, Macau SAR
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17
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Roß T, Bruno P, Reinke A, Wiesenfarth M, Koeppel L, Full PM, Pekdemir B, Godau P, Trofimova D, Isensee F, Adler TJ, Tran TN, Moccia S, Calimeri F, Müller-Stich BP, Kopp-Schneider A, Maier-Hein L. Beyond rankings: Learning (more) from algorithm validation. Med Image Anal 2023; 86:102765. [PMID: 36965252 DOI: 10.1016/j.media.2023.102765] [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: 06/17/2021] [Revised: 05/24/2022] [Accepted: 02/08/2023] [Indexed: 03/06/2023]
Abstract
Challenges have become the state-of-the-art approach to benchmark image analysis algorithms in a comparative manner. While the validation on identical data sets was a great step forward, results analysis is often restricted to pure ranking tables, leaving relevant questions unanswered. Specifically, little effort has been put into the systematic investigation on what characterizes images in which state-of-the-art algorithms fail. To address this gap in the literature, we (1) present a statistical framework for learning from challenges and (2) instantiate it for the specific task of instrument instance segmentation in laparoscopic videos. Our framework relies on the semantic meta data annotation of images, which serves as foundation for a General Linear Mixed Models (GLMM) analysis. Based on 51,542 meta data annotations performed on 2,728 images, we applied our approach to the results of the Robust Medical Instrument Segmentation Challenge (ROBUST-MIS) challenge 2019 and revealed underexposure, motion and occlusion of instruments as well as the presence of smoke or other objects in the background as major sources of algorithm failure. Our subsequent method development, tailored to the specific remaining issues, yielded a deep learning model with state-of-the-art overall performance and specific strengths in the processing of images in which previous methods tended to fail. Due to the objectivity and generic applicability of our approach, it could become a valuable tool for validation in the field of medical image analysis and beyond.
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Affiliation(s)
- Tobias Roß
- Intelligent Medical Systems (IMSY), German Cancer Research Center (DKFZ), Heidelberg, Germany; Medical Faculty, Heidelberg University, Heidelberg, Germany; Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Pierangela Bruno
- Intelligent Medical Systems (IMSY), German Cancer Research Center (DKFZ), Heidelberg, Germany; Department of Mathematics and Computer Science, University of Calabria, Rende, Italy
| | - Annika Reinke
- Intelligent Medical Systems (IMSY), German Cancer Research Center (DKFZ), Heidelberg, Germany; Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Mathematics and Computer Science, Heidelberg University, Germany
| | - Manuel Wiesenfarth
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Lisa Koeppel
- Section Clinical Tropical Medicine, Heidelberg University, Heidelberg, Germany
| | - Peter M Full
- Medical Faculty, Heidelberg University, Heidelberg, Germany; Division of Medical Image Computing (MIC), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Bünyamin Pekdemir
- Intelligent Medical Systems (IMSY), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Patrick Godau
- Intelligent Medical Systems (IMSY), German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Mathematics and Computer Science, Heidelberg University, Germany
| | - Darya Trofimova
- Intelligent Medical Systems (IMSY), German Cancer Research Center (DKFZ), Heidelberg, Germany; HIP Applied Computer Vision Lab, MIC, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Fabian Isensee
- Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany; Division of Medical Image Computing (MIC), German Cancer Research Center (DKFZ), Heidelberg, Germany; HIP Applied Computer Vision Lab, MIC, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Tim J Adler
- Intelligent Medical Systems (IMSY), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Thuy N Tran
- Intelligent Medical Systems (IMSY), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Sara Moccia
- The BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Italy
| | - Francesco Calimeri
- Department of Mathematics and Computer Science, University of Calabria, Rende, Italy
| | - Beat P Müller-Stich
- Department for General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Lena Maier-Hein
- Intelligent Medical Systems (IMSY), German Cancer Research Center (DKFZ), Heidelberg, Germany; Medical Faculty, Heidelberg University, Heidelberg, Germany; Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Mathematics and Computer Science, Heidelberg University, Germany; Germany and National Center for Tumor Diseases (NCT), Heidelberg, Germany
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18
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A multi-centre polyp detection and segmentation dataset for generalisability assessment. Sci Data 2023; 10:75. [PMID: 36746950 PMCID: PMC9902556 DOI: 10.1038/s41597-023-01981-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 01/23/2023] [Indexed: 02/08/2023] Open
Abstract
Polyps in the colon are widely known cancer precursors identified by colonoscopy. Whilst most polyps are benign, the polyp's number, size and surface structure are linked to the risk of colon cancer. Several methods have been developed to automate polyp detection and segmentation. However, the main issue is that they are not tested rigorously on a large multicentre purpose-built dataset, one reason being the lack of a comprehensive public dataset. As a result, the developed methods may not generalise to different population datasets. To this extent, we have curated a dataset from six unique centres incorporating more than 300 patients. The dataset includes both single frame and sequence data with 3762 annotated polyp labels with precise delineation of polyp boundaries verified by six senior gastroenterologists. To our knowledge, this is the most comprehensive detection and pixel-level segmentation dataset (referred to as PolypGen) curated by a team of computational scientists and expert gastroenterologists. The paper provides insight into data construction and annotation strategies, quality assurance, and technical validation.
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19
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Houwen BBSL, Nass KJ, Vleugels JLA, Fockens P, Hazewinkel Y, Dekker E. Comprehensive review of publicly available colonoscopic imaging databases for artificial intelligence research: availability, accessibility, and usability. Gastrointest Endosc 2023; 97:184-199.e16. [PMID: 36084720 DOI: 10.1016/j.gie.2022.08.043] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 08/24/2022] [Accepted: 08/30/2022] [Indexed: 01/28/2023]
Abstract
BACKGROUND AND AIMS Publicly available databases containing colonoscopic imaging data are valuable resources for artificial intelligence (AI) research. Currently, little is known regarding the available number and content of these databases. This review aimed to describe the availability, accessibility, and usability of publicly available colonoscopic imaging databases, focusing on polyp detection, polyp characterization, and quality of colonoscopy. METHODS A systematic literature search was performed in MEDLINE and Embase to identify AI studies describing publicly available colonoscopic imaging databases published after 2010. Second, a targeted search using Google's Dataset Search, Google Search, GitHub, and Figshare was done to identify databases directly. Databases were included if they contained data about polyp detection, polyp characterization, or quality of colonoscopy. To assess accessibility of databases, the following categories were defined: open access, open access with barriers, and regulated access. To assess the potential usability of the included databases, essential details of each database were extracted using a checklist derived from the Checklist for Artificial Intelligence in Medical Imaging. RESULTS We identified 22 databases with open access, 3 databases with open access with barriers, and 15 databases with regulated access. The 22 open access databases contained 19,463 images and 952 videos. Nineteen of these databases focused on polyp detection, localization, and/or segmentation; 6 on polyp characterization, and 3 on quality of colonoscopy. Only half of these databases have been used by other researcher to develop, train, or benchmark their AI system. Although technical details were in general well reported, important details such as polyp and patient demographics and the annotation process were under-reported in almost all databases. CONCLUSIONS This review provides greater insight on public availability of colonoscopic imaging databases for AI research. Incomplete reporting of important details limits the ability of researchers to assess the usability of current databases.
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Affiliation(s)
- Britt B S L Houwen
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam University Medical Centres, location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Karlijn J Nass
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam University Medical Centres, location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Jasper L A Vleugels
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam University Medical Centres, location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Paul Fockens
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam University Medical Centres, location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Yark Hazewinkel
- Department of Gastroenterology and Hepatology, Radboud University Nijmegen Medical Center, Radboud University of Nijmegen, Nijmegen, the Netherlands
| | - Evelien Dekker
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam University Medical Centres, location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
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20
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ELKarazle K, Raman V, Then P, Chua C. Detection of Colorectal Polyps from Colonoscopy Using Machine Learning: A Survey on Modern Techniques. SENSORS (BASEL, SWITZERLAND) 2023; 23:1225. [PMID: 36772263 PMCID: PMC9953705 DOI: 10.3390/s23031225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 01/08/2023] [Accepted: 01/17/2023] [Indexed: 06/18/2023]
Abstract
Given the increased interest in utilizing artificial intelligence as an assistive tool in the medical sector, colorectal polyp detection and classification using deep learning techniques has been an active area of research in recent years. The motivation for researching this topic is that physicians miss polyps from time to time due to fatigue and lack of experience carrying out the procedure. Unidentified polyps can cause further complications and ultimately lead to colorectal cancer (CRC), one of the leading causes of cancer mortality. Although various techniques have been presented recently, several key issues, such as the lack of enough training data, white light reflection, and blur affect the performance of such methods. This paper presents a survey on recently proposed methods for detecting polyps from colonoscopy. The survey covers benchmark dataset analysis, evaluation metrics, common challenges, standard methods of building polyp detectors and a review of the latest work in the literature. We conclude this paper by providing a precise analysis of the gaps and trends discovered in the reviewed literature for future work.
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Affiliation(s)
- Khaled ELKarazle
- School of Information and Communication Technologies, Swinburne University of Technology, Sarawak Campus, Kuching 93350, Malaysia
| | - Valliappan Raman
- Department of Artificial Intelligence and Data Science, Coimbatore Institute of Technology, Coimbatore 641014, India
| | - Patrick Then
- School of Information and Communication Technologies, Swinburne University of Technology, Sarawak Campus, Kuching 93350, Malaysia
| | - Caslon Chua
- Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne 3122, Australia
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21
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U-Net Model with Transfer Learning Model as a Backbone for Segmentation of Gastrointestinal Tract. BIOENGINEERING (BASEL, SWITZERLAND) 2023; 10:bioengineering10010119. [PMID: 36671690 PMCID: PMC9854836 DOI: 10.3390/bioengineering10010119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 01/10/2023] [Accepted: 01/12/2023] [Indexed: 01/18/2023]
Abstract
The human gastrointestinal (GI) tract is an important part of the body. According to World Health Organization (WHO) research, GI tract infections kill 1.8 million people each year. In the year 2019, almost 5 million individuals were detected with gastrointestinal disease. Radiation therapy has the potential to improve cure rates in GI cancer patients. Radiation oncologists direct X-ray beams at the tumour while avoiding the stomach and intestines. The current objective is to direct the X-ray beam toward the malignancy while avoiding the stomach and intestines in order to improve dose delivery to the tumour. This study offered a technique for segmenting GI tract organs (small bowel, big intestine, and stomach) to assist radio oncologists to treat cancer patients more quickly and accurately. The suggested model is a U-Net model designed from scratch and used for the segmentation of a small size of images to extract the local features more efficiently. Furthermore, in the proposed model, six transfer learning models were employed as the backbone of the U-Net topology. The six transfer learning models used are Inception V3, SeResNet50, VGG19, DenseNet121, InceptionResNetV2, and EfficientNet B0. The suggested model was analysed with model loss, dice coefficient, and IoU. The results specify that the suggested model outperforms all transfer learning models, with performance parameter values as 0.122 model loss, 0.8854 dice coefficient, and 0.8819 IoU.
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22
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Ali S. Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions. NPJ Digit Med 2022; 5:184. [PMID: 36539473 PMCID: PMC9767933 DOI: 10.1038/s41746-022-00733-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 11/29/2022] [Indexed: 12/24/2022] Open
Abstract
Recent developments in deep learning have enabled data-driven algorithms that can reach human-level performance and beyond. The development and deployment of medical image analysis methods have several challenges, including data heterogeneity due to population diversity and different device manufacturers. In addition, more input from experts is required for a reliable method development process. While the exponential growth in clinical imaging data has enabled deep learning to flourish, data heterogeneity, multi-modality, and rare or inconspicuous disease cases still need to be explored. Endoscopy being highly operator-dependent with grim clinical outcomes in some disease cases, reliable and accurate automated system guidance can improve patient care. Most designed methods must be more generalisable to the unseen target data, patient population variability, and variable disease appearances. The paper reviews recent works on endoscopic image analysis with artificial intelligence (AI) and emphasises the current unmatched needs in this field. Finally, it outlines the future directions for clinically relevant complex AI solutions to improve patient outcomes.
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Affiliation(s)
- Sharib Ali
- grid.9909.90000 0004 1936 8403School of Computing, University of Leeds, LS2 9JT Leeds, UK
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23
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Turan M, Durmus F. UC-NfNet: Deep learning-enabled assessment of ulcerative colitis from colonoscopy images. Med Image Anal 2022; 82:102587. [DOI: 10.1016/j.media.2022.102587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 07/12/2022] [Accepted: 08/17/2022] [Indexed: 10/31/2022]
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24
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Gupta S, Ali S, Goldsmith L, Turney B, Rittscher J. Multi-class motion-based semantic segmentation for ureteroscopy and laser lithotripsy. Comput Med Imaging Graph 2022; 101:102112. [PMID: 36030620 DOI: 10.1016/j.compmedimag.2022.102112] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 06/09/2022] [Accepted: 07/28/2022] [Indexed: 01/27/2023]
Abstract
Ureteroscopy with laser lithotripsy has evolved as the most commonly used technique for the treatment of kidney stones. Automated segmentation of kidney stones and the laser fiber is an essential initial step to performing any automated quantitative analysis, particularly stone-size estimation, that can be used by the surgeon to decide if the stone requires further fragmentation. However, factors such as turbid fluid inside the cavity, specularities, motion blur due to kidney movements and camera motion, bleeding, and stone debris impact the quality of vision within the kidney, leading to extended operative times. To the best of our knowledge, this is the first attempt made towards multi-class segmentation in ureteroscopy and laser lithotripsy data. We propose an end-to-end convolution neural network (CNN) based learning framework for the segmentation of stones and laser fiber. The proposed approach utilizes two sub-networks: (I) HybResUNet, a hybrid version of residual U-Net, that uses residual connections in the encoder path of the U-Net to improve semantic predictions, and (II) a DVFNet that generates deformation vector field (DVF) predictions by leveraging motion differences between the adjacent video frames which is then used to prune the prediction maps. We also present ablation studies that combine different dilated convolutions, recurrent and residual connections, atrous spatial pyramid pooling, and attention gate models. Further, we propose a compound loss function that significantly boosts the segmentation performance in our data. We have also provided an ablation study to determine the optimal data augmentation strategy for our dataset. Our qualitative and quantitative results illustrate that our proposed method outperforms state-of-the-art methods such as UNet and DeepLabv3+ showing a DSC improvement of 4.15% and 13.34%, respectively, in our in vivo test dataset. We further show that our proposed model outperforms state-of-the-art methods on an unseen out-of-sample clinical dataset with a DSC improvement of 9.61%, 11%, and 5.24% over UNet, HybResUNet, and DeepLabv3+, respectively in the case of the stone class and an improvement of 31.79%, 22.15%, and 10.42% over UNet, HybResUNet, and DeepLabv3+, respectively, in case of the laser class.
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Affiliation(s)
- Soumya Gupta
- Institute of Biomedical Engineering (IBME), Department of Engineering Science, University of Oxford, Oxford, UK; Big Data Institute, University of Oxford, Li Ka Shing Centre for Health Information and Discovery, Oxford, UK.
| | - Sharib Ali
- Institute of Biomedical Engineering (IBME), Department of Engineering Science, University of Oxford, Oxford, UK; Big Data Institute, University of Oxford, Li Ka Shing Centre for Health Information and Discovery, Oxford, UK; Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, UK; School of Computing, University of Leeds, Leeds, UK
| | - Louise Goldsmith
- Department of Urology, The Churchill, Oxford University Hospitals NHS Trust, Oxford, UK
| | - Ben Turney
- Department of Urology, The Churchill, Oxford University Hospitals NHS Trust, Oxford, UK
| | - Jens Rittscher
- Institute of Biomedical Engineering (IBME), Department of Engineering Science, University of Oxford, Oxford, UK; Big Data Institute, University of Oxford, Li Ka Shing Centre for Health Information and Discovery, Oxford, UK; Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, UK; Ludwig Institute for Cancer Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK.
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25
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Yin TK, Huang KL, Chiu SR, Yang YQ, Chang BR. Endoscopy Artefact Detection by Deep Transfer Learning of Baseline Models. J Digit Imaging 2022; 35:1101-1110. [PMID: 35478060 PMCID: PMC9582060 DOI: 10.1007/s10278-022-00627-6] [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: 02/28/2021] [Revised: 03/28/2022] [Accepted: 03/30/2022] [Indexed: 10/18/2022] Open
Abstract
To visualise the tumours inside the body on a screen, a long and thin tube is inserted with a light source and a camera at the tip to obtain video frames inside organs in endoscopy. However, multiple artefacts exist in these video frames that cause difficulty during the diagnosis of cancers. In this research, deep learning was applied to detect eight kinds of artefacts: specularity, bubbles, saturation, contrast, blood, instrument, blur, and imaging artefacts. Based on transfer learning with pre-trained parameters and fine-tuning, two state-of-the-art methods were applied for detection: faster region-based convolutional neural networks (Faster R-CNN) and EfficientDet. Experiments were implemented on the grand challenge dataset, Endoscopy Artefact Detection and Segmentation (EAD2020). To validate our approach in this study, we used phase I of 2,200 frames and phase II of 331 frames in the original training dataset with ground-truth annotations as training and testing dataset, respectively. Among the tested methods, EfficientDet-D2 achieves a score of 0.2008 (mAPd[Formula: see text]0.6+mIoUd[Formula: see text]0.4) on the dataset that is better than three other baselines: Faster-RCNN, YOLOv3, and RetinaNet, and competitive to the best non-baseline result scored 0.25123 on the leaderboard although our testing was on phase II of 331 frames instead of the original 200 testing frames. Without extra improvement techniques beyond basic neural networks such as test-time augmentation, we showed that a simple baseline could achieve state-of-the-art performance in detecting artefacts in endoscopy. In conclusion, we proposed the combination of EfficientDet-D2 with suitable data augmentation and pre-trained parameters during fine-tuning training to detect the artefacts in endoscopy.
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Affiliation(s)
- Tang-Kai Yin
- Department of Computer Science and Information Engineering, National University of Kaohsiung, No. 700, Kaohsiung University Rd., Nan-Tzu Dist., 811, Kaohsiung, Taiwan.
| | - Kai-Lun Huang
- Department of Computer Science and Information Engineering, National University of Kaohsiung, No. 700, Kaohsiung University Rd., Nan-Tzu Dist., 811, Kaohsiung, Taiwan
| | - Si-Rong Chiu
- Department of Computer Science and Information Engineering, National University of Kaohsiung, No. 700, Kaohsiung University Rd., Nan-Tzu Dist., 811, Kaohsiung, Taiwan
| | - Yu-Qi Yang
- Department of Computer Science and Information Engineering, National University of Kaohsiung, No. 700, Kaohsiung University Rd., Nan-Tzu Dist., 811, Kaohsiung, Taiwan
| | - Bao-Rong Chang
- Department of Computer Science and Information Engineering, National University of Kaohsiung, No. 700, Kaohsiung University Rd., Nan-Tzu Dist., 811, Kaohsiung, Taiwan
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26
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Real-time instance segmentation of surgical instruments using attention and multi-scale feature fusion. Med Image Anal 2022; 81:102569. [DOI: 10.1016/j.media.2022.102569] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 07/01/2022] [Accepted: 08/04/2022] [Indexed: 11/18/2022]
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27
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Surgical Tool Datasets for Machine Learning Research: A Survey. Int J Comput Vis 2022. [DOI: 10.1007/s11263-022-01640-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
AbstractThis paper is a comprehensive survey of datasets for surgical tool detection and related surgical data science and machine learning techniques and algorithms. The survey offers a high level perspective of current research in this area, analyses the taxonomy of approaches adopted by researchers using surgical tool datasets, and addresses key areas of research, such as the datasets used, evaluation metrics applied and deep learning techniques utilised. Our presentation and taxonomy provides a framework that facilitates greater understanding of current work, and highlights the challenges and opportunities for further innovative and useful research.
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28
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Fati SM, Senan EM, Azar AT. Hybrid and Deep Learning Approach for Early Diagnosis of Lower Gastrointestinal Diseases. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22114079. [PMID: 35684696 PMCID: PMC9185306 DOI: 10.3390/s22114079] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 05/21/2022] [Accepted: 05/24/2022] [Indexed: 05/27/2023]
Abstract
Every year, nearly two million people die as a result of gastrointestinal (GI) disorders. Lower gastrointestinal tract tumors are one of the leading causes of death worldwide. Thus, early detection of the type of tumor is of great importance in the survival of patients. Additionally, removing benign tumors in their early stages has more risks than benefits. Video endoscopy technology is essential for imaging the GI tract and identifying disorders such as bleeding, ulcers, polyps, and malignant tumors. Videography generates 5000 frames, which require extensive analysis and take a long time to follow all frames. Thus, artificial intelligence techniques, which have a higher ability to diagnose and assist physicians in making accurate diagnostic decisions, solve these challenges. In this study, many multi-methodologies were developed, where the work was divided into four proposed systems; each system has more than one diagnostic method. The first proposed system utilizes artificial neural networks (ANN) and feed-forward neural networks (FFNN) algorithms based on extracting hybrid features by three algorithms: local binary pattern (LBP), gray level co-occurrence matrix (GLCM), and fuzzy color histogram (FCH) algorithms. The second proposed system uses pre-trained CNN models which are the GoogLeNet and AlexNet based on the extraction of deep feature maps and their classification with high accuracy. The third proposed method uses hybrid techniques consisting of two blocks: the first block of CNN models (GoogLeNet and AlexNet) to extract feature maps; the second block is the support vector machine (SVM) algorithm for classifying deep feature maps. The fourth proposed system uses ANN and FFNN based on the hybrid features between CNN models (GoogLeNet and AlexNet) and LBP, GLCM and FCH algorithms. All the proposed systems achieved superior results in diagnosing endoscopic images for the early detection of lower gastrointestinal diseases. All systems produced promising results; the FFNN classifier based on the hybrid features extracted by GoogLeNet, LBP, GLCM and FCH achieved an accuracy of 99.3%, precision of 99.2%, sensitivity of 99%, specificity of 100%, and AUC of 99.87%.
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Affiliation(s)
- Suliman Mohamed Fati
- College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia;
| | - Ebrahim Mohammed Senan
- Department of Computer Science & Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad 431004, India;
| | - Ahmad Taher Azar
- College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia;
- Faculty of Computers and Artificial Intelligence, Benha University, Benha 13518, Egypt
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29
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Khadka R, Jha D, Hicks S, Thambawita V, Riegler MA, Ali S, Halvorsen P. Meta-learning with implicit gradients in a few-shot setting for medical image segmentation. Comput Biol Med 2022; 143:105227. [PMID: 35124439 DOI: 10.1016/j.compbiomed.2022.105227] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 01/05/2022] [Accepted: 01/05/2022] [Indexed: 12/26/2022]
Abstract
Widely used traditional supervised deep learning methods require a large number of training samples but often fail to generalize on unseen datasets. Therefore, a more general application of any trained model is quite limited for medical imaging for clinical practice. Using separately trained models for each unique lesion category or a unique patient population will require sufficiently large curated datasets, which is not practical to use in a real-world clinical set-up. Few-shot learning approaches can not only minimize the need for an enormous number of reliable ground truth labels that are labour-intensive and expensive, but can also be used to model on a dataset coming from a new population. To this end, we propose to exploit an optimization-based implicit model agnostic meta-learning (iMAML) algorithm under few-shot settings for medical image segmentation. Our approach can leverage the learned weights from diverse but small training samples to perform analysis on unseen datasets with high accuracy. We show that, unlike classical few-shot learning approaches, our method improves generalization capability. To our knowledge, this is the first work that exploits iMAML for medical image segmentation and explores the strength of the model on scenarios such as meta-training on unique and mixed instances of lesion datasets. Our quantitative results on publicly available skin and polyp datasets show that the proposed method outperforms the naive supervised baseline model and two recent few-shot segmentation approaches by large margins. In addition, our iMAML approach shows an improvement of 2%-4% in dice score compared to its counterpart MAML for most experiments.
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Affiliation(s)
- Rabindra Khadka
- SimulaMet, Oslo, Norway; Oslo Metropolitan University, Oslo, Norway
| | - Debesh Jha
- SimulaMet, Oslo, Norway; UiT the Arctic University of Norway, Tromsø, Norway.
| | - Steven Hicks
- SimulaMet, Oslo, Norway; Oslo Metropolitan University, Oslo, Norway
| | | | - Michael A Riegler
- SimulaMet, Oslo, Norway; UiT the Arctic University of Norway, Tromsø, Norway
| | - Sharib Ali
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, UK; NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.
| | - Pål Halvorsen
- SimulaMet, Oslo, Norway; Oslo Metropolitan University, Oslo, Norway
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30
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Jin Z, Gan T, Wang P, Fu Z, Zhang C, Yan Q, Zheng X, Liang X, Ye X. Deep learning for gastroscopic images: computer-aided techniques for clinicians. Biomed Eng Online 2022; 21:12. [PMID: 35148764 PMCID: PMC8832738 DOI: 10.1186/s12938-022-00979-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 01/21/2022] [Indexed: 12/13/2022] Open
Abstract
Gastric disease is a major health problem worldwide. Gastroscopy is the main method and the gold standard used to screen and diagnose many gastric diseases. However, several factors, such as the experience and fatigue of endoscopists, limit its performance. With recent advancements in deep learning, an increasing number of studies have used this technology to provide on-site assistance during real-time gastroscopy. This review summarizes the latest publications on deep learning applications in overcoming disease-related and nondisease-related gastroscopy challenges. The former aims to help endoscopists find lesions and characterize them when they appear in the view shed of the gastroscope. The purpose of the latter is to avoid missing lesions due to poor-quality frames, incomplete inspection coverage of gastroscopy, etc., thus improving the quality of gastroscopy. This study aims to provide technical guidance and a comprehensive perspective for physicians to understand deep learning technology in gastroscopy. Some key issues to be handled before the clinical application of deep learning technology and the future direction of disease-related and nondisease-related applications of deep learning to gastroscopy are discussed herein.
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Affiliation(s)
- Ziyi Jin
- Biosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, People's Republic of China
| | - Tianyuan Gan
- Biosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, People's Republic of China
| | - Peng Wang
- Biosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, People's Republic of China
| | - Zuoming Fu
- Biosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, People's Republic of China
| | - Chongan Zhang
- Biosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, People's Republic of China
| | - Qinglai Yan
- Hangzhou Center for Medical Device Quality Supervision and Testing, CFDA, Hangzhou, 310000, People's Republic of China
| | - Xueyong Zheng
- Department of General Surgery, Sir Run-Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, 310016, People's Republic of China
| | - Xiao Liang
- Department of General Surgery, Sir Run-Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, 310016, People's Republic of China
| | - Xuesong Ye
- Biosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, People's Republic of China.
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31
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Kleiner C, Kainz MA, Echternach M, Birkholz P. Velocity differences in laryngeal adduction and abduction gestures. THE JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2022; 151:45. [PMID: 35105025 DOI: 10.1121/10.0009141] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 12/05/2021] [Indexed: 06/14/2023]
Abstract
The periodic repetitions of laryngeal adduction and abduction gestures were uttered by 16 subjects. The movement of the cuneiform tubercles was tracked over time in the laryngoscopic recordings of these utterances. The adduction velocity and abduction velocity were determined objectively by means of a piecewise linear model fitted to the cuneiform tubercle trajectories. The abduction was found to be significantly faster than the adduction. This was interpreted in terms of the biomechanics and active control by the nervous system. The biomechanical properties could be responsible for a velocity of abduction that is up to 51% higher compared to the velocity of adduction. Additionally, the adduction velocity may be actively limited to prevent an overshoot of the intended adduction degree when the vocal folds are approximated to initiate phonation.
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Affiliation(s)
- Christian Kleiner
- Institute of Acoustics and Speech Communication, Technische Universität Dresden, Dresden, Germany
| | - Marie-Anne Kainz
- Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology, Munich University Hospital (LMU), Munich, Germany
| | - Matthias Echternach
- Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology, Munich University Hospital (LMU), Munich, Germany
| | - Peter Birkholz
- Institute of Acoustics and Speech Communication, Technische Universität Dresden, Dresden, Germany
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Srivastava A, Jha D, Chanda S, Pal U, Johansen H, Johansen D, Riegler M, Ali S, Halvorsen P. MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation. IEEE J Biomed Health Inform 2021; 26:2252-2263. [PMID: 34941539 DOI: 10.1109/jbhi.2021.3138024] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Methods based on convolutional neural networks have improved the performance of biomedical image segmentation. However, most of these methods cannot efficiently segment objects of variable sizes and train on small and biased datasets, which are common for biomedical use cases. While methods exist that incorporate multi-scale fusion approaches to address the challenges arising with variable sizes, they usually use complex models that are more suitable for general semantic segmentation problems. In this paper, we propose a novel architecture called Multi-Scale Residual Fusion Network (MSRF-Net), which is specially designed for medical image segmentation. The proposed MSRF-Net is able to exchange multi-scale features of varying receptive fields using a Dual-Scale Dense Fusion (DSDF) block. Our DSDF block can exchange information rigorously across two different resolution scales, and our MSRF sub-network uses multiple DSDF blocks in sequence to perform multi-scale fusion. This allows the preservation of resolution, improved information flow and propagation of both high- and low-level features to obtain accurate segmentation maps. The proposed MSRF-Net allows to capture object variabilities and provides improved results on different biomedical datasets. Extensive experiments on MSRF-Net demonstrate that the proposed method outperforms the cutting edge medical image segmentation methods on four publicly available datasets. We achieve the Dice Coefficient (DSC) of 0.9217, 0.9420, and 0.9224, 0.8824 on Kvasir-SEG, CVC-ClinicDB, 2018 Data Science Bowl dataset, and ISIC-2018 skin lesion segmentation challenge dataset respectively. We further conducted generalizability tests that also achieved the highest DSC score with 0.7921 and 0.7575 on CVC-ClinicDB and Kvasir-SEG, respectively.
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Angeles Ceron JC, Chang L, Ruiz GO, Ali S. Assessing YOLACT++ for real time and robust instance segmentation of medical instruments in endoscopic procedures. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:1824-1827. [PMID: 34891641 DOI: 10.1109/embc46164.2021.9629914] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Image-based tracking of laparoscopic instruments plays a fundamental role in computer and robotic-assisted surgeries by aiding surgeons and increasing patient safety. Computer vision contests, such as the Robust Medical Instrument Segmentation (ROBUST-MIS) Challenge, seek to encourage the development of robust models for such purposes, providing large, diverse, and high-quality datasets. To date, most of the existing models for instance segmentation of medical instruments were based on two-stage detectors, which provide robust results but are nowhere near to the real-time, running at 5 frames-per-second (fps) at most. However, for the method to be clinically applicable, a real-time capability is utmost required along with high accuracy. In this paper, we propose the addition of attention mechanisms to the YOLACT architecture to allow real-time instance segmentation of instruments with improved accuracy on the ROBUST-MIS dataset. Our proposed approach achieves competitive performance compared to the winner of the 2019 ROBUST-MIS challenge in terms of robustness scores, obtaining 0.313 ML_DSC and 0.338 MLNSD while reaching real-time performance at >45 fps.
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Chen D, Fulmer C, Gordon IO, Syed S, Stidham RW, Vande Casteele N, Qin Y, Falloon K, Cohen BL, Wyllie R, Rieder F. Application of Artificial Intelligence to Clinical Practice in Inflammatory Bowel Disease - What the Clinician Needs to Know. J Crohns Colitis 2021; 16:460-471. [PMID: 34558619 PMCID: PMC8919817 DOI: 10.1093/ecco-jcc/jjab169] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Artificial intelligence [AI] techniques are quickly spreading across medicine as an analytical method to tackle challenging clinical questions. What were previously thought of as highly complex data sources, such as images or free text, are now becoming manageable. Novel analytical methods merge the latest developments in information technology infrastructure with advances in computer science. Once primarily associated with Silicon Valley, AI techniques are now making their way into medicine, including in the field of inflammatory bowel diseases [IBD]. Understanding potential applications and limitations of these techniques can be difficult, in particular for busy clinicians. In this article, we explain the basic terminologies and provide a particular focus on the foundations behind state-of-the-art AI methodologies in both imaging and text. We explore the growing applications of AI in medicine, with a specific focus on IBD to inform the practising gastroenterologist and IBD specialist. Finally, we outline possible future uses of these technologies in daily clinical practice.
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Affiliation(s)
- David Chen
- Medical Operations, Cleveland Clinic Foundation, Cleveland, OH, USA,Department of Gastroenterology, Hepatology and Nutrition, Digestive Diseases and Surgery Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Clifton Fulmer
- Department of Pathology, Robert J. Tomsich Pathology and Laboratory Medicine Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Ilyssa O Gordon
- Department of Pathology, Robert J. Tomsich Pathology and Laboratory Medicine Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Sana Syed
- Division of Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, School of Medicine, University of Virginia, Charlottesville, VA, USA,School of Data Science, University of Virginia, Charlottesville, VA, USA
| | - Ryan W Stidham
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Michigan Medicine, Ann Arbor, MI, USA,Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | | | - Yi Qin
- Department of Gastroenterology, Hepatology and Nutrition, Digestive Diseases and Surgery Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Katherine Falloon
- Department of Gastroenterology, Hepatology and Nutrition, Digestive Diseases and Surgery Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Benjamin L Cohen
- Department of Gastroenterology, Hepatology and Nutrition, Digestive Diseases and Surgery Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Robert Wyllie
- Medical Operations, Cleveland Clinic Foundation, Cleveland, OH, USA,Department of Pediatric Gastroenterology, Hepatology, and Nutrition, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Florian Rieder
- Corresponding author: Florian Rieder, MD, Department of Inflammation and Immunity, and Department of Gastroenterology, Hepatology, & Nutrition, Cleveland Clinic Foundation, 9500 Euclid Ave., Cleveland, OH 44195, USA. Tel: (216) 445-5631; Fax: (216) 636-0104; E-mail:
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Gao X, Braden B. Artificial intelligence in endoscopy: The challenges and future directions. Artif Intell Gastrointest Endosc 2021; 2:117-126. [DOI: 10.37126/aige.v2.i4.117] [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: 05/22/2021] [Revised: 06/20/2021] [Accepted: 07/15/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence based approaches, in particular deep learning, have achieved state-of-the-art performance in medical fields with increasing number of software systems being approved by both Europe and United States. This paper reviews their applications to early detection of oesophageal cancers with a focus on their advantages and pitfalls. The paper concludes with future recommendations towards the development of a real-time, clinical implementable, interpretable and robust diagnosis support systems.
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Affiliation(s)
- Xiaohong Gao
- Department of Computer Science, Middlesex University, London NW4 4BT, United Kingdom
| | - Barbara Braden
- Translational Gastroenterology Unit, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, United Kingdom
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36
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Using deep learning to identify the recurrent laryngeal nerve during thyroidectomy. Sci Rep 2021; 11:14306. [PMID: 34253767 PMCID: PMC8275665 DOI: 10.1038/s41598-021-93202-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Accepted: 06/22/2021] [Indexed: 11/16/2022] Open
Abstract
Surgeons must visually distinguish soft-tissues, such as nerves, from surrounding anatomy to prevent complications and optimize patient outcomes. An accurate nerve segmentation and analysis tool could provide useful insight for surgical decision-making. Here, we present an end-to-end, automatic deep learning computer vision algorithm to segment and measure nerves. Unlike traditional medical imaging, our unconstrained setup with accessible handheld digital cameras, along with the unstructured open surgery scene, makes this task uniquely challenging. We investigate one common procedure, thyroidectomy, during which surgeons must avoid damaging the recurrent laryngeal nerve (RLN), which is responsible for human speech. We evaluate our segmentation algorithm on a diverse dataset across varied and challenging settings of operating room image capture, and show strong segmentation performance in the optimal image capture condition. This work lays the foundation for future research in real-time tissue discrimination and integration of accessible, intelligent tools into open surgery to provide actionable insights.
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Gridach M. PyDiNet: Pyramid Dilated Network for medical image segmentation. Neural Netw 2021; 140:274-281. [PMID: 33839599 DOI: 10.1016/j.neunet.2021.03.023] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 03/08/2021] [Accepted: 03/12/2021] [Indexed: 12/18/2022]
Abstract
Medical image segmentation is an important step in many generic applications such as population analysis and, more accessible, can be made into a crucial tool in diagnosis and treatment planning. Previous approaches are based on two main architectures: fully convolutional networks and U-Net-based architecture. These methods rely on multiple pooling and striding layers leading to the loss of important spatial information and fail to capture details in medical images. In this paper, we propose a novel neural network called PyDiNet (Pyramid Dilated Network) to capture small and complex variations in medical images while preserving spatial information. To achieve this goal, PyDiNet uses a newly proposed pyramid dilated module (PDM), which consists of multiple dilated convolutions stacked in parallel. We combine several PDM modules to form the final PyDiNet architecture. We applied the proposed PyDiNet to different medical image segmentation tasks. Experimental results show that the proposed model achieves new state-of-the-art performance on three medical image segmentation benchmarks. Furthermore, PyDiNet was very competitive on the 2020 Endoscopic Artifact Detection challenge.
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Affiliation(s)
- Mourad Gridach
- Department of Computer Science, High Institute of Technology, Agadir, Morocco.
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Jha D, Ali S, Tomar NK, Johansen HD, Johansen D, Rittscher J, Riegler MA, Halvorsen P. Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:40496-40510. [PMID: 33747684 PMCID: PMC7968127 DOI: 10.1109/access.2021.3063716] [Citation(s) in RCA: 69] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 02/15/2021] [Indexed: 05/16/2023]
Abstract
Computer-aided detection, localisation, and segmentation methods can help improve colonoscopy procedures. Even though many methods have been built to tackle automatic detection and segmentation of polyps, benchmarking of state-of-the-art methods still remains an open problem. This is due to the increasing number of researched computer vision methods that can be applied to polyp datasets. Benchmarking of novel methods can provide a direction to the development of automated polyp detection and segmentation tasks. Furthermore, it ensures that the produced results in the community are reproducible and provide a fair comparison of developed methods. In this paper, we benchmark several recent state-of-the-art methods using Kvasir-SEG, an open-access dataset of colonoscopy images for polyp detection, localisation, and segmentation evaluating both method accuracy and speed. Whilst, most methods in literature have competitive performance over accuracy, we show that the proposed ColonSegNet achieved a better trade-off between an average precision of 0.8000 and mean IoU of 0.8100, and the fastest speed of 180 frames per second for the detection and localisation task. Likewise, the proposed ColonSegNet achieved a competitive dice coefficient of 0.8206 and the best average speed of 182.38 frames per second for the segmentation task. Our comprehensive comparison with various state-of-the-art methods reveals the importance of benchmarking the deep learning methods for automated real-time polyp identification and delineations that can potentially transform current clinical practices and minimise miss-detection rates.
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Affiliation(s)
- Debesh Jha
- SimulaMet0167OsloNorway
- Department of Engineering ScienceBig Data Institute, University of OxfordOxfordOX3 7XFU.K.
| | - Sharib Ali
- Department of Engineering ScienceBig Data Institute, University of OxfordOxfordOX3 7XFU.K.
- Oxford NIHR Biomedical Research CentreOxfordOX4 2PGvU.K.
| | | | - Håvard D. Johansen
- Department of Computer ScienceUiT–The Arctic University of Norway9037TromsøNorway
| | - Dag Johansen
- Department of Computer ScienceUiT–The Arctic University of Norway9037TromsøNorway
| | - Jens Rittscher
- Department of Engineering ScienceBig Data Institute, University of OxfordOxfordOX3 7XFU.K.
- Oxford NIHR Biomedical Research CentreOxfordOX4 2PGvU.K.
| | | | - Pål Halvorsen
- SimulaMet0167OsloNorway
- Department of Computer ScienceOslo Metropolitan University0167OsloNorway
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39
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Jha D, Ali S, Hicks S, Thambawita V, Borgli H, Smedsrud PH, de Lange T, Pogorelov K, Wang X, Harzig P, Tran MT, Meng W, Hoang TH, Dias D, Ko TH, Agrawal T, Ostroukhova O, Khan Z, Atif Tahir M, Liu Y, Chang Y, Kirkerød M, Johansen D, Lux M, Johansen HD, Riegler MA, Halvorsen P. A comprehensive analysis of classification methods in gastrointestinal endoscopy imaging. Med Image Anal 2021; 70:102007. [PMID: 33740740 DOI: 10.1016/j.media.2021.102007] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Revised: 01/20/2021] [Accepted: 02/16/2021] [Indexed: 12/24/2022]
Abstract
Gastrointestinal (GI) endoscopy has been an active field of research motivated by the large number of highly lethal GI cancers. Early GI cancer precursors are often missed during the endoscopic surveillance. The high missed rate of such abnormalities during endoscopy is thus a critical bottleneck. Lack of attentiveness due to tiring procedures, and requirement of training are few contributing factors. An automatic GI disease classification system can help reduce such risks by flagging suspicious frames and lesions. GI endoscopy consists of several multi-organ surveillance, therefore, there is need to develop methods that can generalize to various endoscopic findings. In this realm, we present a comprehensive analysis of the Medico GI challenges: Medical Multimedia Task at MediaEval 2017, Medico Multimedia Task at MediaEval 2018, and BioMedia ACM MM Grand Challenge 2019. These challenges are initiative to set-up a benchmark for different computer vision methods applied to the multi-class endoscopic images and promote to build new approaches that could reliably be used in clinics. We report the performance of 21 participating teams over a period of three consecutive years and provide a detailed analysis of the methods used by the participants, highlighting the challenges and shortcomings of the current approaches and dissect their credibility for the use in clinical settings. Our analysis revealed that the participants achieved an improvement on maximum Mathew correlation coefficient (MCC) from 82.68% in 2017 to 93.98% in 2018 and 95.20% in 2019 challenges, and a significant increase in computational speed over consecutive years.
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Affiliation(s)
- Debesh Jha
- SimulaMet, Oslo, Norway; UiT The Arctic University of Norway, Tromsø, Norway.
| | - Sharib Ali
- Department of Engineering Science, University of Oxford, Oxford, UK; Oxford NIHR Biomedical Research Centre, Oxford, UK
| | - Steven Hicks
- SimulaMet, Oslo, Norway; Oslo Metropolitan University, Oslo, Norway
| | | | - Hanna Borgli
- SimulaMet, Oslo, Norway; University of Oslo, Oslo, Norway
| | - Pia H Smedsrud
- SimulaMet, Oslo, Norway; University of Oslo, Oslo, Norway; Augere Medical AS, Oslo, Norway
| | - Thomas de Lange
- SimulaMet, Oslo, Norway; Augere Medical AS, Oslo, Norway; Sahlgrenska University Hospital, Molndal, Sweden; Bærum Hospital, Vestre Viken, Oslo, Norway
| | | | | | | | | | | | | | | | | | | | - Olga Ostroukhova
- Research Institute of Multiprocessor Computation Systems, Russia
| | - Zeshan Khan
- School of Computer Science, National University of Computer and Emerging Sciences, Karachi Campus, Pakistan
| | - Muhammad Atif Tahir
- School of Computer Science, National University of Computer and Emerging Sciences, Karachi Campus, Pakistan
| | - Yang Liu
- Hong Kong Baptist University, Hong Kong
| | - Yuan Chang
- Beijing University of Posts and Telecom., China
| | | | - Dag Johansen
- UiT The Arctic University of Norway, Tromsø, Norway
| | - Mathias Lux
- Alpen-Adria-Universität Klagenfurt, Klagenfurt, Austria
| | | | | | - Pål Halvorsen
- SimulaMet, Oslo, Norway; Oslo Metropolitan University, Oslo, Norway
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