Discarding Non Informative Regions for Efficient Colonoscopy Image Analysis

Bernal, Jorge; Gil, Débora; Sanchez, Carles; Sánchez, Francisco Javier Martín

doi:10.1007/978-3-319-13410-9_1

Cited by 8 publications

(6 citation statements)

References 15 publications

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“…Finally, we evaluate the FCN model on the test set. We compare our results to the combination of previously published handcrafted methods: [ 13 ] an energy map-based method (1) for polyp segmentation and [ 12 ] a watershed-based method (2) for lumen segmentation and [ 15 ] (3) for specular highlights segmentation.…”

Section: Resultsmentioning

confidence: 99%

“…Endoluminal scene segmentation is of crucial relevance for clinical applications [ 6 , 12 – 14 ]. Polyp segmentation is important to define the area covered by a potential lesion that should be carefully inspected and possibly removed by clinicians.…”

Section: Introductionmentioning

confidence: 99%

“…Endoluminal scene segmentation is of crucial relevance for clinical applications [3,4,23,2]. First, segmenting lumen is relevant to help clinicians in navigation during the procedure.…”

mentioning

confidence: 99%

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A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images

Vázquez

Bernal

Sánchez

et al. 2017

Journal of Healthcare Engineering

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499

168

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Colorectal cancer (CRC) is the third cause of cancer death worldwide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss rate and the inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing decision support systems (DSS) aiming to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image segmentation, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. The proposed dataset consists of 4 relevant classes to inspect the endoluminal scene, targeting different clinical needs. Together with the dataset and taking advantage of advances in semantic segmentation literature, we provide new baselines by training standard fully convolutional networks (FCNs). We perform a comparative study to show that FCNs significantly outperform, without any further postprocessing, prior results in endoluminal scene segmentation, especially with respect to polyp segmentation and localization.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images

Vázquez

Bernal

Sánchez

et al. 2017

Journal of Healthcare Engineering

Self Cite

499

168

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“…On the one hand, an accurate detection of the lumen region during in-vivo intervention may be useful to discard areas of the image with low visibility - Fig. 17(a) -in order to save computation time for other interesting regions of the image as proposed in [44]. Lumen detection can also be helpful to guide the clinician inside the intestine by pointing out which direction he/she should take to progress.…”

Section: Luminal Areamentioning

confidence: 99%

Building up the Future of Colonoscopy – A Synergy between Clinicians and Computer Scientists

Bernal¹,

Sánchez²,

Miguel³

et al. 2015

Screening for Colorectal Cancer With Colonoscopy

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Recent advances in endoscopic technology have generated an increasing interest in strengthening the collaboration between clinicians and computers scientist to develop intelligent systems that can provide additional information to clinicians in the different stages of an intervention. The objective of this chapter is to identify clinical drawbacks of colonoscopy in order to define potential areas of collaboration. Once areas are defined, we present the challenges that colonoscopy images present in order computational methods to provide with meaningful output, including those related to image formation and acquisition, as they are proven to have an impact in the performance of an intelligent system. Finally, we also propose how to define validation frameworks in order to assess the performance of a given method, making an special emphasis on how databases should be created and annotated and which metrics should be used to evaluate systems correctly.

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“…In [159], an efficient domain-specific algorithm is proposed to detect the exact circle parameters. Bernal et al [20] propose a model of appearance of non-informative lumen regions that can be discarded in a subsequent CAD (Computer Aided Diagnosis) component. Prasath et al [181] also use image segmentation to differentiate between lumen and mucosa, but they use the result as a basis for 3D reconstruction.…”

Section: Image Segmentationmentioning

confidence: 99%

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

Münzer

Schoeffmann

Böszörményi

2017

Multimed Tools Appl

114

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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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Discarding Non Informative Regions for Efficient Colonoscopy Image Analysis

Cited by 8 publications

References 15 publications

A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images

A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images

Building up the Future of Colonoscopy – A Synergy between Clinicians and Computer Scientists

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

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