Intelligent visual localization of wireless capsule endoscopes enhanced by color information

Dimas, George; Spyrou, Evaggelos; Iakovidis, Dimitris K.; Koulaouzidis, Anastasios

doi:10.1016/j.compbiomed.2017.08.029

Cited by 29 publications

(20 citation statements)

References 19 publications

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“…described use of an unsupervised CNN that estimates both distance travelled and size of lesions seen on WCE by analysing the captured images only, with no specialized equipment. This paper expands on earlier work by the same group, though with improved accuracy 46 . In their study, an unaltered capsule in a lifelike artificial bowel was used 21 .…”

Section: Measurement and Capsule Localizationmentioning

confidence: 70%

“…Lesion size estimation is entirely visual, based on the proportion of the screen taken up relative to the surrounding normal small bowel. Several machine learning approaches to localization and lesion measurement based on image analysis have been tested as far back as 2008 but have not been incorporated into commercially available products 43–46 …”

Section: Measurement and Capsule Localizationmentioning

confidence: 99%

See 1 more Smart Citation

Artificial intelligence and deep learning for small bowel capsule endoscopy

Trasolini

Byrne

2020

Digestive Endoscopy

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Capsule endoscopy is ideally suited to artificial intelligencebased interpretation given its reliance on pattern recognition in still images. Time saving viewing modes and lesion detection features currently available rely on machine learning algorithms, a form of artificial intelligence. Current software necessitates close human supervision given poor sensitivity relative to an expert reader. However, with the advent of deep learning, artificial intelligence is becoming increasingly reliable and will be increasingly relied upon. We review the major advances in artificial intelligence for capsule endoscopy in recent publications and briefly review artificial intelligence development for historical understanding. Importantly, recent advancements in artificial intelligence have not yet been incorporated into practice and it is immature to judge the potential of this technology based on current platforms. Remaining regulatory and standardization hurdles are being overcome and artificial intelligence-based clinical applications are likely to proliferate rapidly.

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Section: Measurement and Capsule Localizationmentioning

confidence: 70%

Section: Measurement and Capsule Localizationmentioning

confidence: 99%

Artificial intelligence and deep learning for small bowel capsule endoscopy

Trasolini

Byrne

2020

Digestive Endoscopy

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“…These can track the motion of the CE by tracking image features frame by frame. In recent works, ANNs have been used to accurately estimate the motion of the CE in physical units, based solely on image features 30,31 . The main challenges for this approach include the motility of the GI tract and the presence of intestinal content, which can affect the localization performance.…”

Section: Localization and Size Measurementmentioning

confidence: 99%

“…In recent works, ANNs have been used to accurately estimate the motion of the CE in physical units, based solely on image features. 30,31 The main challenges for this approach include the motility of the GI tract and the presence of intestinal content, which can affect the localization performance. Such methods have also been used as a basis to develop other useful methods in GI endoscopy, including the reconstruction of the GI tract for visualization, 32 and the precise measurement of size of a lesion in vivo during a CE examination.…”

Section: Localization and Size Measurementmentioning

confidence: 99%

Artificial intelligence in small bowel capsule endoscopy ‐ current status, challenges and future promise

Dray

Iakovidis

Houdeville

et al. 2021

J of Gastro and Hepatol

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Neural network‐based solutions are under development to alleviate physicians from the tedious task of small‐bowel capsule endoscopy reviewing. Computer‐assisted detection is a critical step, aiming to reduce reading times while maintaining accuracy. Weakly supervised solutions have shown promising results; however, video‐level evaluations are scarce, and no prospective studies have been conducted yet. Automated characterization (in terms of diagnosis and pertinence) by supervised machine learning solutions is the next step. It relies on large, thoroughly labeled databases, for which preliminary “ground truth” definitions by experts are of tremendous importance. Other developments are under ways, to assist physicians in localizing anatomical landmarks and findings in the small bowel, in measuring lesions, and in rating bowel cleanliness. It is still questioned whether artificial intelligence will enter the market with proprietary, built‐in or plug‐in software, or with a universal cloud‐based service, and how it will be accepted by physicians and patients.

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“…Development in image processing and deep learning have provided another framework for localization of the endoscopy capsule. It has been demonstrated that, based on geometrical models, pure visual aided localization can be performed in vitro [28][29][30][31][32]. In particular Wahid et al [19] and Bao et al [20] provided a simple geometrical approximation to the colon.…”

Section: Introductionmentioning

confidence: 99%

Feature Point Tracking-Based Localization of Colon Capsule Endoscope

et al. 2021

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In large bowel investigations using endoscopic capsules and upon detection of significant findings, physicians require the location of those findings for a follow-up therapeutic colonoscopy. To cater to this need, we propose a model based on tracking feature points in consecutive frames of videos retrieved from colon capsule endoscopy investigations. By locally approximating the colon as a cylinder, we obtained both the displacement and the orientation of the capsule using geometrical assumptions and by setting priors on both physical properties of the intestine and the image sample frequency of the endoscopic capsule. Our proposed model tracks a colon capsule endoscope through the large intestine for different prior selections. A discussion on validating the findings in terms of intra and inter capsule and expert panel validation is provided. The performance of the model is evaluated based on the average difference in multiple reconstructed capsule’s paths through the large intestine. The path difference averaged over all videos was as low as 4±0.7 cm, with min and max error corresponding to 1.2 and 6.0 cm, respectively. The inter comparison addresses frame classification for the rectum, descending and sigmoid, splenic flexure, transverse, hepatic, and ascending, with an average accuracy of 86%.

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Intelligent visual localization of wireless capsule endoscopes enhanced by color information

Cited by 29 publications

References 19 publications

Artificial intelligence and deep learning for small bowel capsule endoscopy

Artificial intelligence and deep learning for small bowel capsule endoscopy

Artificial intelligence in small bowel capsule endoscopy ‐ current status, challenges and future promise

Feature Point Tracking-Based Localization of Colon Capsule Endoscope

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