Automated sizing of colorectal polyps using computer vision

Abdelrahim, Mohamed; Saiga, Hiroyasu; Maeda, Naoto; Hossain, Ejaz; Ikeda, Hitoshi; Bhandari, Pradeep

doi:10.1136/gutjnl-2021-324510

Cited by 22 publications

(21 citation statements)

References 4 publications

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“…Artificial Intelligence (AI) has been proposed as an alternative for polyp size measurement in one study 17 . However, the authors state that due to the difficulty of acquiring datasets with ground truth information on polyp size AI based systems are difficult to develop.…”

Section: Discussionmentioning

confidence: 99%

“…However, the authors state that due to the difficulty of acquiring datasets with ground truth information on polyp size AI based systems are difficult to develop. Instead of presenting measurement accuracy based on deviation of obtained measurement from true size, the authors chose to test if the AI-based system can distinguish > 5 mm polyps from diminutive polyps which was possible in 80 % 17 . Similar to VSE, AI-based technology would have the advantage of not necessitating additional tools for polyp sizing.…”

Section: Discussionmentioning

confidence: 99%

“…Instead of presenting measurement accuracy based on deviation of obtained measurement from true size, the authors chose to test if the AI based system can distinguish >5mm polyps from diminutive polyps which was possible in 80%. [17] Similar to VSE, AI based technology would have the advantage of not necessitating additional tools for polyp sizing. However, to date no AI system that can be used in live endoscopy is available and systems for clinical implementation will have to provide measurement granularity in millimeter size ranges.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

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Comparing size measurement of colorectal polyps using a novel virtual scale endoscope, endoscopic ruler or forceps: A preclinical randomized trial

et al. 2023

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Background and Aims: Accurate polyp size measurement is important for guideline conforming choice of polypectomy techniques and subsequent surveillance interval assignments. Some endoscopic tools (biopsy forceps [BF] or endoscopic rulers [ER]) exist to help with visual size estimation. A virtual scale endoscope (VSE) has been developed that allows superimposing a virtual measurement scale during live endoscopies. Our aim was to evaluate the performance of VSE when compared to ER and BF-based measurement. Methods: We conducted a preclinical randomized trial to evaluate the relative accuracy of size measurement of simulated colorectal polyps when using: VSE, ER, and BF. Six endoscopists performed 60 measurements randomized at a 1:1:1 ratio using each method. Primary outcome was relative accuracy in polyp size measurement. Secondary outcomes included misclassification of sizes at the 5, 10, and 20mm thresholds. Results: A total of 360 measurements were performed. The relative accuracy of BF, ER, and VSE was 78.9% (95%CI=76.2-81.5), 78.4% (95%CI=76.0-80.8), and 82.7% (95%CI=80.8-84.8). VSE had significantly higher accuracy compared to BF (p=0.02) and ER (p=0.006). VSE misclassified a lower percentage of polyps >5mm as ≤5mm (9.4%) compared to BF (15.7%) and ER (20.9%). VSE misclassified a lower percentage of ≥20mm polyps as <20mm (8.3%) compared with BF (66.7%) and ER (75.0%). 25.6%, 25.5%, and 22.5% of polyps ≥10mm were misclassified as <10mm with ER, BF, and VSE, respectively. Conclusions: VSE had significantly higher relative accuracy in measuring polyps compared to ER or biopsy forceps assisted measurement. VSE improves correct classification of polyps at clinically important size thresholds.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Accepted Manuscriptmentioning

confidence: 99%

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Comparing size measurement of colorectal polyps using a novel virtual scale endoscope, endoscopic ruler or forceps: A preclinical randomized trial

et al. 2023

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“…Indeed, Requa et al [ 81 ] developed a highly accurate convolutional neural network to estimate the size of polyps in colonoscopy, dividing them into three size-based groups of ≤5, 6–9, and ≥10 mm (model accuracy: 97%, 97%, and 98%, respectively). More recently, Abdelrahim et al [ 82 ] developed a deep learning model based on convolutional neural networks (CNN) with an 80% accuracy in real-time polyp sizing. Such systems when integrated to CADx could improve polyp management strategies.…”

Section: Artificial Intelligence In Colonoscopymentioning

confidence: 99%

Artificial Intelligence-Aided Endoscopy and Colorectal Cancer Screening

et al. 2023

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Colorectal cancer (CRC) is the third most common cancer worldwide, with the highest incidence reported in high-income countries. However, because of the slow progression of neoplastic precursors, along with the opportunity for their endoscopic detection and resection, a well-designed endoscopic screening program is expected to strongly decrease colorectal cancer incidence and mortality. In this regard, quality of colonoscopy has been clearly related with the risk of post-colonoscopy colorectal cancer. Recently, the development of artificial intelligence (AI) applications in the medical field has been growing in interest. Through machine learning processes, and, more recently, deep learning, if a very high numbers of learning samples are available, AI systems may automatically extract specific features from endoscopic images/videos without human intervention, helping the endoscopists in different aspects of their daily practice. The aim of this review is to summarize the current knowledge on AI-aided endoscopy, and to outline its potential role in colorectal cancer prevention.

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“…Holes in [30]. Other relevant applications of 3D computer vision techniques in colonoscopy include polyp size prediction [31], surface topography reconstruction [32], visual odometry estimation [33], and enhanced lesion classification with 3D augmentation [34].…”

Section: Durr Is the Corresponding Authormentioning

confidence: 99%

Colonoscopy 3D Video Dataset with Paired Depth from 2D-3D Registration

Bobrow¹,

Golhar²,

Vijayan³

et al. 2022

Preprint

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Screening colonoscopy is an important clinical application for several 3D computer vision techniques, including depth estimation, surface reconstruction, and missing region detection. However, the development, evaluation, and comparison of these techniques in real colonoscopy videos remain largely qualitative due to the difficulty of acquiring ground truth data. In this work, we present a Colonoscopy 3D Video Dataset (C3VD) acquired with a high definition clinical colonoscope and high-fidelity colon models for benchmarking computer vision methods in colonoscopy. We introduce a novel multimodal 2D-3D registration technique to register optical video sequences with ground truth rendered views of a known 3D model. The different modalities are registered by transforming optical images to depth maps with a Generative Adversarial Network and aligning edge features with an evolutionary optimizer. This registration method achieves an average translation error of 0.321 millimeters and an average rotation error of 0.159 degrees in simulation experiments where error-free ground truth is available. The method also leverages video information, improving registration accuracy by 55.6% for translation and 60.4% for rotation compared to single frame registration. 22 short video sequences were registered to generate 10,015 total frames with paired ground truth depth, surface normals, optical flow, occlusion, six degree-of-freedom pose, coverage maps, and 3D models. The dataset also includes screening videos acquired by a gastroenterologist with paired ground truth pose and 3D surface models. The dataset and registration source code are available at durr.jhu.edu/C3VD.

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Automated sizing of colorectal polyps using computer vision

Cited by 22 publications

References 4 publications

Comparing size measurement of colorectal polyps using a novel virtual scale endoscope, endoscopic ruler or forceps: A preclinical randomized trial

Comparing size measurement of colorectal polyps using a novel virtual scale endoscope, endoscopic ruler or forceps: A preclinical randomized trial

Artificial Intelligence-Aided Endoscopy and Colorectal Cancer Screening

Colonoscopy 3D Video Dataset with Paired Depth from 2D-3D Registration

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