WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians

Bernal, Jorge; Sánchez, Francisco Javier Martín; Fernández‐Esparrach, Glòria; Gil, Débora; Rodrı́guez, Cristina; Vilariño, Fernando

doi:10.1016/j.compmedimag.2015.02.007

Cited by 1,002 publications

(562 citation statements)

References 25 publications

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“…Apart from the continuous improvement of clinicians' skills through training programs and practice [14], technical efforts are being undertaken to improve colonoscopy's outcome. We clustered them into two groups: improvement of devices and the development of computational support systems.…”

Section: B Technical Strategies To Improve Polyp Detection Ratementioning

confidence: 99%

Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results From the MICCAI 2015 Endoscopic Vision Challenge

Bernal

Tajkbaksh

Sánchez

et al. 2017

IEEE Trans. Med. Imaging

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344

276

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Abstract-Colonoscopy is the gold standard for colon cancer screening though still some polyps are missed, thus preventing early disease detection and treatment. Several computational systems have been proposed to assist polyp detection during colonoscopy but so far without consistent evaluation. The lack of publicly available annotated databases has made it difficult to compare methods and to assess if they achieve performance levels acceptable for clinical use. The Automatic Polyp Detection subchallenge, conducted as part of the Endoscopic Vision Challenge (http://endovis.grand-challenge.org) at the international conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2015, was an effort to address this need. In this paper, we report the results of this comparative evaluation of polyp detection methods, as well as describe additional experiments to further explore differences between methods. We define performance metrics and provide evaluation databases that allow comparison of multiple methodologies. Results show that convolutional neural networks (CNNs) are the state of the art. Nevertheless it is also demonstrated that combining different methodologies can lead to an improved overall performance.

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Section: B Technical Strategies To Improve Polyp Detection Ratementioning

confidence: 99%

Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results From the MICCAI 2015 Endoscopic Vision Challenge

Bernal

Tajkbaksh

Sánchez

et al. 2017

IEEE Trans. Med. Imaging

Self Cite

344

276

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show abstract

“…Numerous methods for automatic polyp detection in colonoscopy have been proposed, 3,4 the majority of which can be roughly grouped in two categories: texture/colour based and shape based. Recently, deep learning approaches were successfully applied for polyp detection in colonoscopy videos.…”

Section: Introductionmentioning

confidence: 99%

Fully convolutional neural networks for polyp segmentation in colonoscopy

et al. 2017

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Colorectal cancer (CRC) is one of the most common and deadliest forms of cancer, accounting for nearly 10% of all forms of cancer in the world. Even though colonoscopy is considered the most effective method for screening and diagnosis, the success of the procedure is highly dependent on the operator skills and level of hand-eye coordination. In this work, we propose to adapt fully convolution neural networks (FCN), to identify and segment polyps in colonoscopy images. We converted three established networks into a fully convolution architecture and fine-tuned their learned representations to the polyp segmentation task. We validate our framework on the 2015 MICCAI polyp detection challenge dataset, surpassing the state-of-the-art in automated polyp detection. Our method obtained high segmentation accuracy and a detection precision and recall of 73.61% and 86.31%, respectively.

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“…Interval cancers are those that appear between two scheduled diagnostic tests and, in most cases, are due to a polyp or tumour that was not detected by the specialist during the procedure. In this context, publications such as [1][2][3] have made important contributions to the scientific community.…”

Section: Applicability Of Pre-processing Colonoscopic Imagery In Robotsmentioning

confidence: 99%

Colonoscopy Image Pre-Processing for the Development of Computer-Aided Diagnostic Tools

Sánchez-González¹,

Soto²

2018

Surgical Robotics

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Colorrectal cancer is the third most frequently diagnosed cancer worldwide. The American Cancer Society estimates that there will be almost 100,000 new patients diagnosed with colorectal cancer and that around 50,000 people will die as a consequence of this in 2016. The increase of life expectancy and the increment of the number of diagnostic tests conducted have had a great impact on the amount of cancers being detected. Among other diagnostic tools, colonoscopy is the most prevalent. In order to help endoscopists cope with the increasing amount of tests that have to be carried out, there exists a need to develop automated tools that aid diagnosis. The characteristics of the colon make preprocessing essential to eliminate artefacts that degrade the quality of exploratory images. The goal of this chapter is to describe the most common issues of colonoscopic imagery as well the existing methods for their optimal detection and correction.

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WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians

Cited by 1,002 publications

References 25 publications

Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results From the MICCAI 2015 Endoscopic Vision Challenge

Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results From the MICCAI 2015 Endoscopic Vision Challenge

Fully convolutional neural networks for polyp segmentation in colonoscopy

Colonoscopy Image Pre-Processing for the Development of Computer-Aided Diagnostic Tools

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