Automatic Labeling of Colonoscopy Video for Cancer Detection

Vilariño, Fernando; Lacey, Gerard; Zhou, Jiang; Mulcahy, Hugh; Patchett, Stephen

doi:10.1007/978-3-540-72847-4_38

Cited by 17 publications

(27 citation statements)

References 11 publications

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“…The visual analysis of these data provided promising results in the clustering of the different types of polyps into three basic types (peduncular, flat and mixed). For a further analysis of these results we refer to the IbPria conference proceedings (Vilariño et al, 2007).…”

Section: Resultsmentioning

confidence: 99%

“…In recent works we presented preliminary results of discriminative features and classification systems for colon cancer detection and its a posteriori characterization in different types of polyps. Figure 2 shows examples of a) peduncular, b) flat and c) mixed polyps, which have a different degree of clinical relevance, as appearing in our latest contribution (Vilariño et al, 2007). Our next step is, hence, to put these techniques in the horizon of real-time performance.…”

Section: Colon Cancer: Clinical Characterizationmentioning

confidence: 99%

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QUALITY ASSESSMENT IN COLONOSCOPY - New Challenges Through Computer Vision-based Systems

Vilariño¹,

Lacey²

2009

Proceedings of the International Conference on Biomedical Electronics and Devices

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The assessment of the quality of the colonoscopic interventions arises as a most relevant issue once the number and the availability of these clinical procedures are increased day by day. The use of the latest computer visionbased techniques can provide the physician with both qualitative and, most important, objectively verifiable quantitative indicators of performance. In this paper we present a study in which we propose the automatic analysis of colonoscopy video for the quality assessment of the intervention from different points of view: 1) We propose the characterization of the different parts of the colon in order to obtain metrics of the time used for navigation, portion of gut analyzed, etc. 2) We analyze the image contents in order to automatically characterize the presence of polyps. 3) We use the information obtained by and eye-tracker in order to assess the physician's skills.

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Section: Resultsmentioning

confidence: 99%

Section: Colon Cancer: Clinical Characterizationmentioning

confidence: 99%

QUALITY ASSESSMENT IN COLONOSCOPY - New Challenges Through Computer Vision-based Systems

Vilariño¹,

Lacey²

2009

Proceedings of the International Conference on Biomedical Electronics and Devices

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“…If the annotation task consist of the definition of ROIs, a mouse, a digital pen, or a tactile device can be used. More sophisticated techniques, such as the use of eye-tracking (Vilariño et al (2007)), can be implemented in case that the video is to be annotated by using attention/perception models -see Figure 7 b) for a general scheme.…”

Section: Annotation Of Colonoscopy Videomentioning

confidence: 99%

Towards Intelligent Systems for Colonoscopy

Bernal¹,

Vilariño²,

Sánchez³

2011

Colonoscopy

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“…Vilariño et al proposed a promising architecture for automatic labeling of colonoscopy video for cancer detection using eye movement of the observer, where results from gaze tracking were used to select regions in the images [8].…”

Section: Related Workmentioning

confidence: 99%

Texture-Based Polyp Detection in Colonoscopy

et al. 2009

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Abstract. Colonoscopy is one of the best methods for screening colon cancer. A variety of research groups have proposed methods for automatic detection of polyps in colonoscopic images to support the doctors during examination. However, the problem can still not be assumed as solved. The major drawback of many approaches is the amount and quality of images used for classifier training and evaluation. Our database consists of more than four hours of high resolution video from colonoscopies which were examined and labeled by medical experts. We applied four methods of texture feature extraction based on Grey-LevelCo-occurence and Local-Binary-Patterns. Using this data, we achieved classification results with an area under the ROC-curve of up to 0.96.

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Automatic Labeling of Colonoscopy Video for Cancer Detection

Cited by 17 publications

References 11 publications

QUALITY ASSESSMENT IN COLONOSCOPY - New Challenges Through Computer Vision-based Systems

QUALITY ASSESSMENT IN COLONOSCOPY - New Challenges Through Computer Vision-based Systems

Towards Intelligent Systems for Colonoscopy

Texture-Based Polyp Detection in Colonoscopy

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