Quantitative analysis and development of a computer-aided system for identification of regular pit patterns of colorectal lesions

Takemura, Y; Yoshida, Shigeto; Tanaka, Shinji; Onji, Keiichi; Oka, Shiro; Tamaki, Toru; Kaneda, Kazufumi; Yoshihara, Masaharu; Chayama, Kazuaki

doi:10.1016/j.gie.2010.07.037

Cited by 64 publications

(44 citation statements)

References 2 publications

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“…The diagnostic accuracy for distinguishing nonadenomatous from adenomatous polyps was 70%. The diagnostic accuracy for our CNN-CAD system is not satisfactory at present as compared to previous reports [7][8][9][10][11][12][13][14][15][16] . In this regard, we assume that deep learning with 600 or 1,200 images might not be sufficient and, therefore, attribute the relatively low performance to the low number of images.…”

Section: Discussioncontrasting

confidence: 80%

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Computer-Aided Diagnosis Based on Convolutional Neural Network System for Colorectal Polyp Classification: Preliminary Experience

Komeda

Handa²,

Watanabe³

et al. 2017

Oncology

186

111

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Background and Aim: Computer-aided diagnosis (CAD) is becoming a next-generation tool for the diagnosis of human disease. CAD for colon polyps has been suggested as a particularly useful tool for trainee colonoscopists, as the use of a CAD system avoids the complications associated with endoscopic resections. In addition to conventional CAD, a convolutional neural network (CNN) system utilizing artificial intelligence (AI) has been developing rapidly over the past 5 years. We attempted to generate a unique CNN-CAD system with an AI function that studied endoscopic images extracted from movies obtained with colonoscopes used in routine examinations. Here, we report our preliminary results of this novel CNN-CAD system for the diagnosis of colon polyps. Methods: A total of 1,200 images from cases of colonoscopy performed between January 2010 and December 2016 at Kindai University Hospital were used. These images were extracted from the video of actual endoscopic examinations. Additional video images from 10 cases of unlearned processes were retrospectively assessed in a pilot study. They were simply diagnosed as either an adenomatous or nonadenomatous polyp. Results: The number of images used by AI to learn to distinguish adenomatous from nonadenomatous was 1,200:600. These images were extracted from the videos of actual endoscopic examinations. The size of each image was adjusted to 256 × 256 pixels. A 10-hold cross-validation was carried out. The accuracy of the 10-hold cross-validation is 0.751, where the accuracy is the ratio of the number of correct answers over the number of all the answers produced by the CNN. The decisions by the CNN were correct in 7 of 10 cases. Conclusion: A CNN-CAD system using routine colonoscopy might be useful for the rapid diagnosis of colorectal polyp classification. Further prospective studies in an in vivo setting are required to confirm the effectiveness of a CNN-CAD system in routine colonoscopy.

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Section: Discussioncontrasting

confidence: 80%

“…The CAD of colon polyps has been reported on previously [7][8][9][10][11][12][13][14][15][16] . Previous studies have reported the usefulness of CAD for colon polyps.…”

Section: Introductionmentioning

confidence: 89%

Computer-Aided Diagnosis Based on Convolutional Neural Network System for Colorectal Polyp Classification: Preliminary Experience

Komeda

Handa²,

Watanabe³

et al. 2017

Oncology

186

111

View full text Add to dashboard Cite

show abstract

“…Other work from this group includes texture analysis with wavelet transforms (Häfner et al, 2009f;Kwitt and Uhl, 2007a), Gabor wavelets (Kwitt and Uhl, 2007b), histograms (Häfner et al, 2006), and others. In our previous work (Takemura et al, 2010), we have used shape analysis of extracted pits, such as area, perimeter, major and minor axes of a fit ellipse, diameter, and circularity.…”

Section: Related Work and Contributionsmentioning

confidence: 99%

Computer-aided colorectal tumor classification in NBI endoscopy using local features

Tamaki

Yoshimuta

Kawakami

et al. 2013

Medical Image Analysis

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131

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“…Nevertheless, such pit pattern classification is subjective and based on experience, and quantification is difficult. We recently described quantification and computer-aided detection of the regular pit pattens of colorectal lesions; the type V pit pattern, including subtypes V I and V N , was not included in that study [38]. Furthermore, no group has yet described quantitative analysis in relation to the histopathologic features of colorectal tumors.…”

Section: Discussionmentioning

confidence: 99%

Quantitative analysis of colorectal lesions observed on magnified endoscopy images

et al. 2011

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Quantitative analysis and development of a computer-aided system for identification of regular pit patterns of colorectal lesions

Abstract: Our system is best characterized as semiautomated but is a step toward the development of a fully automated system to assist in the diagnosis of colorectal lesions based on classification of pit patterns.

Cited by 64 publications

References 2 publications

Computer-Aided Diagnosis Based on Convolutional Neural Network System for Colorectal Polyp Classification: Preliminary Experience

Computer-Aided Diagnosis Based on Convolutional Neural Network System for Colorectal Polyp Classification: Preliminary Experience

Computer-aided colorectal tumor classification in NBI endoscopy using local features

Quantitative analysis of colorectal lesions observed on magnified endoscopy images

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