In Vivo Assessment of Tumor Vascularity Using Confocal Laser Endomicroscopy in Murine Models of Colon Cancer

Konda, Vani J.; Becker, Valentin; Ruderman, Sarah; Dougherty, Urszula; Hart, John; Ruiz, Mariano Gonzalez Haba; Valuckaite, Vesta; Kulkarni, Anirudh; Fichera, Alessandro; Waxman, Irving; Bissonnette, Marc

doi:10.2174/221155281120100007

Cited by 2 publications

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“…Vessel detection was proposed in 8 after a Hessian-based filter in addition to length area and diameter measurements of vascular crypts of the colon’s wall. Blood vessels of the colon’s wall characterized in 9 from Fourier analysis. Also, vascular networks of colon’s wall were characterized in terms of graphs in 10 after skeletonization on few hundreds of images.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Classification of the Health State of Mice Colon in Cancer Study from Confocal Laser Endomicroscopy

Rasti

Wolf

Dorez

et al. 2019

Sci Rep

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In this article, we address the problem of the classification of the health state of the colon’s wall of mice, possibly injured by cancer with machine learning approaches. This problem is essential for translational research on cancer and is a priori challenging since the amount of data is usually limited in all preclinical studies for practical and ethical reasons. Three states considered including cancer, health, and inflammatory on tissues. Fully automated machine learning-based methods are proposed, including deep learning, transfer learning, and shallow learning with SVM. These methods addressed different training strategies corresponding to clinical questions such as the automatic clinical state prediction on unseen data using a pre-trained model, or in an alternative setting, real-time estimation of the clinical state of individual tissue samples during the examination. Experimental results show the best performance of 99.93% correct recognition rate obtained for the second strategy as well as the performance of 98.49% which were achieved for the more difficult first case.

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Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Classification of the Health State of Mice Colon in Cancer Study from Confocal Laser Endomicroscopy

Rasti

Wolf

Dorez

et al. 2019

Sci Rep

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“…Several studies have tried to understand the role of CLE in the evaluation of the microvascular architecture of the GI mucosa, but data have often been obtained ex vivo on biopsies or on animal models . The aim of the present study was to assess the role of probe‐based confocal laser endomicroscopy (p‐CLE) for in vivo detection of superficial vascular architecture during routine endoscopy, identifying specific patterns in normal colorectal mucosa and malignant lesions.…”

Section: Introductionmentioning

confidence: 99%

In vivo assessment of tumour angiogenesis in colorectal cancer: the role of confocal laser endomicroscopy

et al. 2016

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In Vivo Assessment of Tumor Vascularity Using Confocal Laser Endomicroscopy in Murine Models of Colon Cancer

Cited by 2 publications

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Machine Learning-Based Classification of the Health State of Mice Colon in Cancer Study from Confocal Laser Endomicroscopy

Machine Learning-Based Classification of the Health State of Mice Colon in Cancer Study from Confocal Laser Endomicroscopy

In vivo assessment of tumour angiogenesis in colorectal cancer: the role of confocal laser endomicroscopy

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