Towards Real-Time Polyp Detection in Colonoscopy Videos: Adapting Still Frame-Based Methodologies for Video Sequences Analysis

Angermann, Quentin; Bernal, Jorge; Sánchez-Montes, Cristina; Hammami, Maroua; Fernández‐Esparrach, Glòria; Dray, Xavier; Romain, Olivier; Sánchez, Francisco Javier Martín; Histace, Aymeric

doi:10.1007/978-3-319-67543-5_3

Cited by 68 publications

(55 citation statements)

References 14 publications

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“…Several groups have since used the annotated datasets to enhance methods. 42,43 Figure 1 illustrates the polyp detection performance of different CNNs. Results from the subsequent 2017 challenge await publication.…”

Section: Wang Et Al Developed An Algorithm That Extracted Features Fmentioning

confidence: 99%

Artificial intelligence and computer-aided diagnosis in colonoscopy: current evidence and future directions

Ahmad

Soares

Mazomenos

et al. 2019

The Lancet Gastroenterology & Hepatology

176

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Computer-aided diagnosis (CAD) offers a promising solution to reduce variation in colonoscopy performance. Pooled miss rates as high as 22% for polyps and associated interval colorectal cancers following colonoscopy are concerning. Meanwhile, the concept of 'optical biopsy' where in vivo classification of polyps based on enhanced imaging replaces histopathology has not been incorporated into routine practice, largely limited by inter-observer variability and generally meeting accepted standards only in expert settings. Real-time decision support software has been developed to detect and characterise polyps, whilst also offering feedback on the technical quality of inspection. Some of the current algorithms, particularly with recent advances in artificial intelligence techniques, now match human expert performance for optical biopsy. This article will review the current evidence in relation to the clinical applications of CAD and artificial intelligence in colonoscopy.

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Section: Wang Et Al Developed An Algorithm That Extracted Features Fmentioning

confidence: 99%

Artificial intelligence and computer-aided diagnosis in colonoscopy: current evidence and future directions

Ahmad

Soares

Mazomenos

et al. 2019

The Lancet Gastroenterology & Hepatology

176

View full text Add to dashboard Cite

show abstract

“…In this study, we used publicly available polyp-frame dataset, CVC-CLINIC [35], and a colonoscopy video databases, CVC-ClinicVideoDB dataset [36].…”

Section: Experimental Datasetsmentioning

confidence: 99%

Abnormal Colon Polyp Image Synthesis Using Conditional Adversarial Networks for Improved Detection Performance

2018

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One of the major obstacles in automatic polyp detection during colonoscopy is the lack of labeled polyp training images. In this study, we propose a framework of conditional adversarial networks to increase the number of training samples by generating synthetic polyp images. Using a normal binary form of polyp mask which represents only the polyp position as an input conditioned image, realistic polyp image generation is a difficult task in generative adversarial networks approach. Therefore, we propose an edge filtering based combined input conditioned image. More importantly, our proposed framework generates synthetic polyp images from normal colonoscopy images which have the advantage of being relatively easy to obtain. This means realistic polyp images can be generated while maintaining the original structures of the colonoscopy image frames. The network architecture is based on the use of multiple dilated convolutions in each encoding part of our generator network to consider large receptive fields and avoid much contractions of feature map size. An image resizing with convolution for up sampling in the decoding layers is considered to prevent artifacts on generated images. We show that the generated polyp images are not only qualitatively look realistic but also help to improve polyp detection performance.INDEX TERMS Colonoscopy, convolutional neural network, dilated convolution, generative adversarial networks, polyp detection.

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“…19,20 Table 1 shows the summary of the clinical studies of AI for the detection of colorectal polyps. [18][19][20][21][22][23][24][25][26] Urban et al 20 reported the first real-time application. They initially pretrained AI using ImageNet and then trained the deep CNNs.…”

Section: Studies Related To Application Of Ai In Colonos-copy 1 Ai Fmentioning

confidence: 99%

Application of Artificial Intelligence in the Detection and Characterization of Colorectal Neoplasm

Kim

2021

Gut and Liver

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Endoscpists always have tried to pursue a perfect colonoscopy, and application of artificial intelligence (AI) using deep-learning algorithms is one of the promising supportive options for detection and characterization of colorectal polyps during colonoscopy. Many retrospective studies conducted with real-time application of AI using convolutional neural networks have shown improved colorectal polyp detection. Moreover, a recent randomized clinical trial reported additional polyp detection with shorter analysis time. Studies conducted regarding polyp characterization provided additional promising results. Application of AI with narrow band imaging in real-time prediction of the pathology of diminutive polyps resulted in high diagnostic accuracy. In addition, application of AI with endocytoscopy or confocal laser endomicroscopy was investigated for real-time cellular diagnosis, and the diagnostic accuracy of some studies was comparable to that of pathologists. With AI technology, we can expect a higher polyp detection rate with reduced time and cost by avoiding unnecessary procedures, resulting in enhanced colonoscopy efficiency. However, for AI application in actual daily clinical practice, more prospective studies with minimized selection bias, consensus on standardized utilization, and regulatory approval are needed.

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Towards Real-Time Polyp Detection in Colonoscopy Videos: Adapting Still Frame-Based Methodologies for Video Sequences Analysis

Cited by 68 publications

References 14 publications

Artificial intelligence and computer-aided diagnosis in colonoscopy: current evidence and future directions

Artificial intelligence and computer-aided diagnosis in colonoscopy: current evidence and future directions

Abnormal Colon Polyp Image Synthesis Using Conditional Adversarial Networks for Improved Detection Performance

Application of Artificial Intelligence in the Detection and Characterization of Colorectal Neoplasm

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