Generic feature learning for wireless capsule endoscopy analysis

Segu, Santi; Drozdzal, Michal; Pascual, Guillem; Radeva, Petia; Malagelada, Carolina; Azpiroz, Fernando; Vitri, Jordi

doi:10.1016/j.compbiomed.2016.10.011

Cited by 98 publications

(62 citation statements)

References 44 publications

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“…The review presented only Zhou's study which has achieved a sensitivity and specificity of 100%. Seguí et al in (Seguí et al, 2016) presented a generic feature descriptor for the classification of video capsule endoscopy images. In order to build the system they created a large database containing only color images, designed a CNN architecture and performed an exhaustive validation of the proposed method.…”

Section: Ai Techniquesmentioning

confidence: 99%

Current Evidence on Computer-Aided Diagnosis of Celiac Disease: Systematic Review

et al. 2020

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Celiac disease (CD) is a chronic autoimmune disease that occurs in genetically predisposed individuals in whom the ingestion of gluten leads to damage of the small bowel. It is estimated to affect 1 in 100 people worldwide, but is severely underdiagnosed. Currently available guidelines require CD-specific serology and atrophic histology in duodenal biopsy samples for the diagnosis of adult CD. In pediatric CD, but in recent years in adults also, nonbioptic diagnostic strategies have become increasingly popular. In this setting, in order to increase the diagnostic rate of this pathology, endoscopy itself has been thought of as a case finding strategy by use of digital image processing techniques. Research focused on computer aided decision support used as database video capsule, endoscopy and even biopsy duodenal images. Early automated methods for diagnosis of celiac disease used feature extraction methods like spatial domain features, transform domain features, scale-invariant features and spatio-temporal features. Recent artificial intelligence (AI) techniques using deep learning (DL) methods such as convolutional neural network (CNN), support vector machines (SVM) or Bayesian inference have emerged as a breakthrough computer technology which can be used for computer aided diagnosis of celiac disease. In the current review we summarize methods used in clinical studies for classification of CD from feature extraction methods to AI techniques.

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Section: Ai Techniquesmentioning

confidence: 99%

Current Evidence on Computer-Aided Diagnosis of Celiac Disease: Systematic Review

et al. 2020

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“…A method using superpixel segmentation and naive Bayes classifier for bleeding frames detection was recently proposed in [43]. In [23,41,51], systems for small intestine motility characterization and bleeding detection, based on deep convolutional neural networks were introduced. The CE scores to assess small-bowel inflammatory activity in Crohn's disease were evaluated in [37].…”

Section: Related Workmentioning

confidence: 99%

Computer‐aided diagnosis system for ulcer detection in wireless capsule endoscopy images

Charfi

Ansari

Balasingham

2019

IET Image Processing

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Wireless capsule endoscopy (WCE) has revolutionized the diagnosis and treatment of gastrointestinal tract, especially the small intestine which is unreachable by traditional endoscopies. The drawback of the WCE is that it produces a large amount of images to be inspected by the clinicians. Hence, the design of a computer-aided diagnosis (CAD) system will have a great potential to help reducing the diagnosis time and improve the detection accuracy. To address this problem, we propose a CAD system for automatic detection of ulcer in WCE images. Firstly, we enhance the input images to be better exploited in the main steps of the proposed method. Afterwards, segmentation using saliency map based texture and colour is applied to the WCE images in order to highlight ulcerous regions. Then, inspired by the existing feature extraction approaches, a new one has been proposed for the recognition of the segmented regions. Finally, a new recognition scheme is proposed based on hidden markov model using the classification scores of the conventional methods (support vector machine, multilayer perceptron and random forest) as observations. Experimental results with two different datasets show that the proposed method gives promising results.

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“…polyps, ulcers, tumors and bleeding). Extensive reviews of these CAD systems can be found in [3,4,9]. Rapid Reader commercial software has been one of the most used diagnostic support tool since it provides, amongst other tools, the Suspected Blood Indicator (SBI), which identifies frames with possible red lesions in the gastrointestinal (GI) tract, based on color.…”

Section: Introductionmentioning

confidence: 99%

“…Computational methods for automatic image processing and analysis, such as smoothing filters, noise removal, contour detection or segmentation, can be used to facilitate the detection of anomalies/pathologies and to homogenize the response between different clinicians. Since 2012, Convolutional Neural Network (CNN), commonly known as "deep learning", started to present significantly better results than previous methods, automatically extracting characteristics from data and thus supporting new developments in CAD systems [9]. In this paper, an evaluation of deep learning U-Net architecture is presented for detecting and segmenting red lesions in the small bowel.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach for Red Lesions Detection in Video Capsule Endoscopies

Coelho

Pereira

Leite

et al. 2018

Lecture Notes in Computer Science

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The wireless capsule endoscopy has revolutionized early diagnosis of small bowel diseases. However, a single examination has up to 10 h of video and requires between 30-120 min to read. Computational methods are needed to increase both efficiency and accuracy of the diagnosis. In this paper, an evaluation of deep learning U-Net architecture is presented, to detect and segment red lesions in the small bowel. Its results were compared with those obtained from the literature review. To make the evaluation closer to those used in clinical environments, the U-Net was also evaluated in an annotated sequence by using the Suspected Blood Indicator tool (SBI). Results found that detection and segmentation using U-Net outperformed both the algorithms used in the literature review and the SBI tool.

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Generic feature learning for wireless capsule endoscopy analysis

Cited by 98 publications

References 44 publications

Current Evidence on Computer-Aided Diagnosis of Celiac Disease: Systematic Review

Current Evidence on Computer-Aided Diagnosis of Celiac Disease: Systematic Review

Computer‐aided diagnosis system for ulcer detection in wireless capsule endoscopy images

A Deep Learning Approach for Red Lesions Detection in Video Capsule Endoscopies

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