Polyp-Alert: Near real-time feedback during colonoscopy

Wang, Yi; Tavanapong, Wallapak; Wong, Johnny; Oh, Jung Hwan; Groen, Piet C. de

doi:10.1016/j.cmpb.2015.04.002

Cited by 114 publications

(58 citation statements)

References 47 publications

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“…Endoscopy is the most common method for identifying colon polyps and several studies have shown that automatic detection of image regions which may contain polyps within the colon can be used to assist specialists in order to decrease the polyp miss rate [1, 2]. …”

Section: Introductionmentioning

confidence: 99%

Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification

Ribeiro

Uhl

Wimmer

et al. 2016

Computational and Mathematical Methods in Medicine

145

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Recently, Deep Learning, especially through Convolutional Neural Networks (CNNs) has been widely used to enable the extraction of highly representative features. This is done among the network layers by filtering, selecting, and using these features in the last fully connected layers for pattern classification. However, CNN training for automated endoscopic image classification still provides a challenge due to the lack of large and publicly available annotated databases. In this work we explore Deep Learning for the automated classification of colonic polyps using different configurations for training CNNs from scratch (or full training) and distinct architectures of pretrained CNNs tested on 8-HD-endoscopic image databases acquired using different modalities. We compare our results with some commonly used features for colonic polyp classification and the good results suggest that features learned by CNNs trained from scratch and the “off-the-shelf” CNNs features can be highly relevant for automated classification of colonic polyps. Moreover, we also show that the combination of classical features and “off-the-shelf” CNNs features can be a good approach to further improve the results.

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Section: Introductionmentioning

confidence: 99%

Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification

Ribeiro

Uhl

Wimmer

et al. 2016

Computational and Mathematical Methods in Medicine

145

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“…This system, called Polyp-Alert [52], is able to give near real-time feedback during colonoscopies. It uses an advanced edge-finding procedure to locate visual features and a rule-based classifier to detect an edge along the contour of a polyp.…”

Section: Related Workmentioning

confidence: 99%

“…The approach from Wang et al [52] is the most recent and probably best-working system in the field of polyp detection. This system, called Polyp-Alert [52], is able to give near real-time feedback during colonoscopies.…”

Section: Related Workmentioning

confidence: 99%

“…In this scenario, both high precision and recall are important, but so is the frequently ignored system performance in order to provide feedback in real-time. The most recent and most complete related work is the Polyp-Alert polyp detection system [52], which can provide near real-time feedback during colonoscopies. However, it is limited to polyp detection, it uses edges, colors and texture in the images, and, at the moment, it is not fast enough for live examinations.…”

Section: Introductionmentioning

confidence: 99%

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Efficient disease detection in gastrointestinal videos – global features versus neural networks

Pogorelov

Riegler

Eskeland

et al. 2017

Multimed Tools Appl

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Analysis of medical videos from the human gastrointestinal (GI) tract for detection and localization of abnormalities like lesions and diseases requires both high precision and recall. Additionally, it is important to support efficient, real-time processing for live feedback during (i) standard colonoscopies and (ii) scalability for massive population-based screening, which we conjecture can be done using a wireless video capsule endoscope (camera-pill). Existing related work in this field does neither provide the necessary combination of accuracy and performance for detecting multiple classes of abnormalities simultaneously nor for particular disease localization tasks. In this paper, a complete end-toend multimedia system is presented where the aim is to tackle automatic analysis of GI tract videos. The system includes an entire pipeline ranging from data collection, processing and analysis, to visualization. The system combines deep learning neural networks, information retrieval, and analysis of global and local image features in order to implement multi-class classification, detection and localization. Furthermore, it is built in a modular way, so that it can be easily extended to deal with other types of abnormalities. Simultaneously, the system is developed for efficient processing in order to provide real-time feedback to the doctors and for scalability reasons when potentially applied for massive population-based algorithmic screenings in the future. Initial experiments show that our system has multiclass detection accuracy and polyp localization precision at least as good as state-of-the-art systems, and provides additional novelty in terms of real-time performance, low resource consumption and ability to extend with support for new classes of diseases.

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“…Such decision support systems are often called CAD (Computer Aided Diagnosis) systems and are already used to some extent in clinical practice. In general, CAD systems strive to be real-time capable to provide immediate feedback during the examination, e.g., [261]. If the physician misses a suspicious structure, the system can highlight the corresponding region to indicate that it should be investigated in detail and maybe a biopsy should be taken.…”

Section: Diagnostic Decision Supportmentioning

confidence: 99%

Content-based processing and analysis of endoscopic images and videos: A survey

Münzer

Schoeffmann

Böszörményi

2017

Multimed Tools Appl

112

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In recent years, digital endoscopy has established as key technology for medical screenings and minimally invasive surgery. Since then, various research communities with manifold backgrounds have picked up on the idea of processing and automatically analyzing the inherently available video signal that is produced by the endoscopic camera. Proposed works mainly include image processing techniques, pattern recognition, machine learning methods and Computer Vision algorithms. While most contributions deal with realtime assistance at procedure time, the post-procedural processing of recorded videos is still in its infancy. Many post-processing problems are based on typical Multimedia methods like indexing, retrieval, summarization and video interaction, but have only been sparsely addressed so far for this domain. The goals of this survey are (1) to introduce this research field to a broader audience in the Multimedia community to stimulate further research, (2) to describe domain-specific characteristics of endoscopic videos that need to be addressed in a pre-processing step, and (3) to systematically bring together the very diverse research results for the first time to provide a broader overview of related research that is currently not perceived as belonging together.

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Polyp-Alert: Near real-time feedback during colonoscopy

Cited by 114 publications

References 47 publications

Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification

Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification

Efficient disease detection in gastrointestinal videos – global features versus neural networks

Content-based processing and analysis of endoscopic images and videos: A survey

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