Toward real-time polyp detection using fully CNNs for 2D Gaussian shapes prediction

Qadir, Hemin Ali; Shin, Younghak; Sølhusvik, Johannes; Bergsland, Jacob; Aabakken, Lars; Balasingham, Ilangko

doi:10.1016/j.media.2020.101897

Cited by 52 publications

(19 citation statements)

References 19 publications

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“…Twodimensional Gaussians are also used in other scientific disciplines such as physics (Kravtsov & Berczynski, 2004;Z. Wu & Guo, 1998), materials sciences (Riekel et al, 1999), and image processing (Hanumantharaju et al, 2013;Ketenci & Gencturk, 2013), particularly in medical imaging (Qadir et al, 2021;J. Wu et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

gaussplotR: Fit, Predict and Plot 2D-Gaussians in R

Baliga¹

2021

JOSS

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Section: Discussionmentioning

confidence: 99%

gaussplotR: Fit, Predict and Plot 2D-Gaussians in R

Baliga¹

2021

JOSS

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“…On the other hand, works indicating the location of polyps as binary marks use encoder-decoder architectures for object segmentation like SegNet or Unet [50,51]. Recently, Qadir et al [52] reported a real-time polyp detection model based on an encoder-decoder architecture named MDeNetplus that converts binary masks into bounding boxes for polyp localization. Finally, Urban et al [32] presented polyp localization as a regression problem, training different network architectures (VGG16, VGG19, and ResNet50) to predict the size and location of the bounding boxes.…”

Section: State Of the Artmentioning

confidence: 99%

Real-time polyp detection model using convolutional neural networks

Nogueira-Rodríguez

Domínguez-Carbajales

Campos-Tato

et al. 2021

Neural Comput & Applic

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Colorectal cancer is a major health problem, where advances towards computer-aided diagnosis (CAD) systems to assist the endoscopist can be a promising path to improvement. Here, a deep learning model for real-time polyp detection based on a pre-trained YOLOv3 (You Only Look Once) architecture and complemented with a post-processing step based on an object-tracking algorithm to reduce false positives is reported. The base YOLOv3 network was fine-tuned using a dataset composed of 28,576 images labelled with locations of 941 polyps that will be made public soon. In a frame-based evaluation using isolated images containing polyps, a general F1 score of 0.88 was achieved (recall = 0.87, precision = 0.89), with lower predictive performance in flat polyps, but higher for sessile, and pedunculated morphologies, as well as with the usage of narrow band imaging, whereas polyp size < 5 mm does not seem to have significant impact. In a polyp-based evaluation using polyp and normal mucosa videos, with a positive criterion defined as the presence of at least one 50-frames-length (window size) segment with a ratio of 75% of frames with predicted bounding boxes (frames positivity), 72.61% of sensitivity (95% CI 68.99–75.95) and 83.04% of specificity (95% CI 76.70–87.92) were achieved (Youden = 0.55, diagnostic odds ratio (DOR) = 12.98). When the positive criterion is less stringent (window size = 25, frames positivity = 50%), sensitivity reaches around 90% (sensitivity = 89.91%, 95% CI 87.20–91.94; specificity = 54.97%, 95% CI 47.49–62.24; Youden = 0.45; DOR = 10.76). The object-tracking algorithm has demonstrated a significant improvement in specificity whereas maintaining sensitivity, as well as a marginal impact on computational performance. These results suggest that the model could be effectively integrated into a CAD system.

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“…In addition, 5 articles were obtained after screening the published articles of related systematic reviews and meta-analysis, which totaled 714 records. After 284 articles were excluded as duplicates and 372 articles were excluded on the basis of titles and abstracts, 15 studies on polyps detection (9,12,13,(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31), 10 studies on polyps classification (32)(33)(34)(35)(36)(37)(38)(39)(40)(41), and 1 (42) which was a combination of both were identified as being appropriate for full-text review. The process of literature screening and inclusion is shown in Figure 1.…”

Section: Literature Screeningmentioning

confidence: 99%

“…Studies in this systematic review included 15 preclinical studies on polyps detection (9,12,13,(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31), 10 preclinical studies on polyps classification (32)(33)(34)(35)(36)(37)(38)(39)(40)(41), and 1 (42) which was a combination of both. In terms of the detection of polyps, 5 studies exploring the (9,23,26-28) the performance of realtime AI-assisted detection reported no TN.…”

Section: The Basic Characteristics Of the Included Literaturementioning

confidence: 99%

“…In terms of the detection of polyps, 5 studies exploring the (9,23,26-28) the performance of realtime AI-assisted detection reported no TN. There were 4 studies that (13,20,22,30) used pictures or videos with polyps to verify AI-assisted detection performance, in which the reported number of TN was 0. However, the other trials all reported TN.…”

Section: The Basic Characteristics Of the Included Literaturementioning

confidence: 99%

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Artificial intelligence-assisted detection and classification of colorectal polyps under colonoscopy: a systematic review and meta-analysis

Wang¹,

Mo²,

Zhong³

et al. 2021

Ann Transl Med

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Background: Artificial intelligence (AI) is used to solve the problem of missed diagnosis of polyps in colonoscopy, which has been proved to improve the detection rate of adenomas. The aim of this review was to evaluate the diagnostic performance of AI-assisted detection and classification of polyps in colonoscopy. Methods:The literature search was undertaken on 4 electronic databases (PubMed, Web of Science, Embase, and Cochrane Library). The inclusion criteria were as follows: studies reporting AI-assisted detection and classification of polyps; studies containing patients, images, or videos receiving AI-assisted diagnosis; studies which included AI-assisted diagnosis and reported classification based on histopathology; and studies providing accurate diagnostic data. Non-English language studies, case-reports, reviews, meeting abstracts and so on were excluded. The Quality Assessment of Diagnostic Accuracy Studies-2 scale was used to evaluate the quality of literature and the Stata 13.0 software was used to perform meta-analysis.Results: Twenty-six articles were included with all of medium quality. Meta-analysis showed none of literature had any obvious publication bias. The application of AI in detection of colorectal polyps achieved a sensitivity of 0.95 [95% confidence interval (CI): 0.89-0.98] and an area under the curve (AUC) of 0.79 (95% CI: 0.79-0.82). In the AI-assisted classification, the sensitivity was 0.92 (95% CI: 0.88-0.95) with a specificity of 0.82 (95% CI: 0.71-0.89) and an AUC of 0.94 (95% CI: 0.92-0.96). For the classification of diminutive polyps, the AI-assisted technique yielded a sensitivity of 0.95 (95% CI: 0.94-0.97), a specificity of 0.88 (95% CI: 0.74-0.95), and an AUC of 0.97 (95% CI: 0.95-0.98). For AI-assisted classification under magnifying endoscopy, the sensitivity was 0.954 (95% CI: 0.92-0.96) with a specificity of 0.95 (95% CI: 0.80-0.99) and an AUC of 0.97 (95% CI: 0.95-0.98).Discussion: The AI-assisted technique demonstrates impressive accuracy for the detection and characterization of colorectal polyps and can be expected to be a novel auxiliary diagnosis method. Our study has inevitable limitations including heterogeneity due to different AI systems and the inability to further analyze the specificity and sensitivity of AI for different types of endoscopes.

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Toward real-time polyp detection using fully CNNs for 2D Gaussian shapes prediction

Cited by 52 publications

References 19 publications

gaussplotR: Fit, Predict and Plot 2D-Gaussians in R

gaussplotR: Fit, Predict and Plot 2D-Gaussians in R

Real-time polyp detection model using convolutional neural networks

Artificial intelligence-assisted detection and classification of colorectal polyps under colonoscopy: a systematic review and meta-analysis

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