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

Bernal, Jorge; Tajkbaksh, Nima; Sánchez, Francisco Javier Martín; Matuszewski, Bogdan J.; Chen, Hao; Yu, Lequan; Angermann, Quentin; Romain, Olivier; Rustad, Bjorn; Balasingham, Ilangko; Pogorelov, Konstantin; Choi, Sungbin; Debard, Quentin; Maier-Hein, Lena; Speidel, Stefanie; Stoyanov, Danail; Brandão, Patrick; Córdova, Henry; Sánchez-Montes, Cristina; Gurudu, Suryakanth R.; Fernández‐Esparrach, Glòria; Dray, Xavier; Liang, Jianming; Histace, Aymeric

doi:10.1109/tmi.2017.2664042

Cited by 324 publications

(268 citation statements)

References 46 publications

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“…Most of the data have been collected from Department of Electronics, University of Alcala (http://www.depeca.uah.es/colonoscopy_dataset/) [16]. Another important source of data set is Endoscopic Vision Challenge (https://polyp.grand-challenge.org/databases/) [22]. Also the proposed system is assessed against standard dataset.…”

Section: Resultsmentioning

confidence: 99%

An Automatic Gastrointestinal Polyp Detection System in Video Endoscopy Using Fusion of Color Wavelet and Convolutional Neural Network Features

Billah

Waheed

Rahman

2017

International Journal of Biomedical Imaging

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Gastrointestinal polyps are considered to be the precursors of cancer development in most of the cases. Therefore, early detection and removal of polyps can reduce the possibility of cancer. Video endoscopy is the most used diagnostic modality for gastrointestinal polyps. But, because it is an operator dependent procedure, several human factors can lead to misdetection of polyps. Computer aided polyp detection can reduce polyp miss detection rate and assists doctors in finding the most important regions to pay attention to. In this paper, an automatic system has been proposed as a support to gastrointestinal polyp detection. This system captures the video streams from endoscopic video and, in the output, it shows the identified polyps. Color wavelet (CW) features and convolutional neural network (CNN) features of video frames are extracted and combined together which are used to train a linear support vector machine (SVM). Evaluations on standard public databases show that the proposed system outperforms the state-of-the-art methods, gaining accuracy of 98.65%, sensitivity of 98.79%, and specificity of 98.52%.

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Section: Resultsmentioning

confidence: 99%

An Automatic Gastrointestinal Polyp Detection System in Video Endoscopy Using Fusion of Color Wavelet and Convolutional Neural Network Features

Billah

Waheed

Rahman

2017

International Journal of Biomedical Imaging

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“…Therefore, automatic polyp detection is important research and can be helpful to improve clinician's performance as a diagnostic supporting tool. Recently, with the success of deep learning in many image processing and computer vision applications, convolutional neural network (CNN) based deep learning approaches have been proposed for polyp detection [3][4] [5]. Yet, the detection performance is still not acceptable for use in clinical tools compared to other object detection tasks in natural image domains.…”

Section: Introductionmentioning

confidence: 99%

Abnormal Colon Polyp Image Synthesis Using Conditional Adversarial Networks for Improved Detection Performance

2018

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One of the major obstacles in automatic polyp detection during colonoscopy is the lack of labeled polyp training images. In this study, we propose a framework of conditional adversarial networks to increase the number of training samples by generating synthetic polyp images. Using a normal binary form of polyp mask which represents only the polyp position as an input conditioned image, realistic polyp image generation is a difficult task in generative adversarial networks approach. Therefore, we propose an edge filtering based combined input conditioned image. More importantly, our proposed framework generates synthetic polyp images from normal colonoscopy images which have the advantage of being relatively easy to obtain. This means realistic polyp images can be generated while maintaining the original structures of the colonoscopy image frames. The network architecture is based on the use of multiple dilated convolutions in each encoding part of our generator network to consider large receptive fields and avoid much contractions of feature map size. An image resizing with convolution for up sampling in the decoding layers is considered to prevent artifacts on generated images. We show that the generated polyp images are not only qualitatively look realistic but also help to improve polyp detection performance.INDEX TERMS Colonoscopy, convolutional neural network, dilated convolution, generative adversarial networks, polyp detection.

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“…Despite significant progress in recent years, the best method in the 2015 MICCAI sub-challenge on automatic polyp detection, the Figure 1: Examples of polyp images from the 2015 MICCAI sub-challenge on automatic polyp detection. 7 The contours of the manual polyp segmentations are overlaid in black lines.…”

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

Cited by 324 publications

References 46 publications

An Automatic Gastrointestinal Polyp Detection System in Video Endoscopy Using Fusion of Color Wavelet and Convolutional Neural Network Features

An Automatic Gastrointestinal Polyp Detection System in Video Endoscopy Using Fusion of Color Wavelet and Convolutional Neural Network Features

Abnormal Colon Polyp Image Synthesis Using Conditional Adversarial Networks for Improved Detection Performance

Fully convolutional neural networks for polyp segmentation in colonoscopy

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