Deep learning for discovering pathological continuum of crypts and evaluating therapeutic effects: An implication for in vivo preclinical study.

Applying deep learning to the field of preclinical in vivo studies is a new and exciting prospect with the potential to unlock decades worth of underutilized data. As a proof of concept, we performed a feasibility study on a colitis model treated with Sulfasalazine, a drug used in therapeutic care o...

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Autores principales:	Dechao Shan, Jie Zheng, Alexander Klimowicz, Mark Panzenbeck, Zheng Liu, Di Feng
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9792d8cf3d574844bf4b1922ca50e9e1
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spelling	oai:doaj.org-article:9792d8cf3d574844bf4b1922ca50e9e12021-12-02T20:10:42ZDeep learning for discovering pathological continuum of crypts and evaluating therapeutic effects: An implication for in vivo preclinical study.1932-620310.1371/journal.pone.0252429https://doaj.org/article/9792d8cf3d574844bf4b1922ca50e9e12021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0252429https://doaj.org/toc/1932-6203Applying deep learning to the field of preclinical in vivo studies is a new and exciting prospect with the potential to unlock decades worth of underutilized data. As a proof of concept, we performed a feasibility study on a colitis model treated with Sulfasalazine, a drug used in therapeutic care of inflammatory bowel disease. We aimed to evaluate the colonic mucosa improvement associated with the recovery response of the crypts, a complex histologic structure reflecting tissue homeostasis and repair in response to inflammation. Our approach requires robust image segmentation of objects of interest from whole slide images, a composite low dimensional representation of the typical or novel morphological variants of the segmented objects, and exploration of image features of significance towards biology and treatment efficacy. Both interpretable features (eg. counts, area, distance and angle) as well as statistical texture features calculated using Gray Level Co-Occurance Matrices (GLCMs), are shown to have significance in analysis. Ultimately, this analytic framework of supervised image segmentation, unsupervised learning, and feature analysis can be generally applied to preclinical data. We hope our report will inspire more efforts to utilize deep learning in preclinical in vivo studies and ultimately make the field more innovative and efficient.Dechao ShanJie ZhengAlexander KlimowiczMark PanzenbeckZheng LiuDi FengPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0252429 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Dechao Shan Jie Zheng Alexander Klimowicz Mark Panzenbeck Zheng Liu Di Feng Deep learning for discovering pathological continuum of crypts and evaluating therapeutic effects: An implication for in vivo preclinical study.
description	Applying deep learning to the field of preclinical in vivo studies is a new and exciting prospect with the potential to unlock decades worth of underutilized data. As a proof of concept, we performed a feasibility study on a colitis model treated with Sulfasalazine, a drug used in therapeutic care of inflammatory bowel disease. We aimed to evaluate the colonic mucosa improvement associated with the recovery response of the crypts, a complex histologic structure reflecting tissue homeostasis and repair in response to inflammation. Our approach requires robust image segmentation of objects of interest from whole slide images, a composite low dimensional representation of the typical or novel morphological variants of the segmented objects, and exploration of image features of significance towards biology and treatment efficacy. Both interpretable features (eg. counts, area, distance and angle) as well as statistical texture features calculated using Gray Level Co-Occurance Matrices (GLCMs), are shown to have significance in analysis. Ultimately, this analytic framework of supervised image segmentation, unsupervised learning, and feature analysis can be generally applied to preclinical data. We hope our report will inspire more efforts to utilize deep learning in preclinical in vivo studies and ultimately make the field more innovative and efficient.
format	article
author	Dechao Shan Jie Zheng Alexander Klimowicz Mark Panzenbeck Zheng Liu Di Feng
author_facet	Dechao Shan Jie Zheng Alexander Klimowicz Mark Panzenbeck Zheng Liu Di Feng
author_sort	Dechao Shan
title	Deep learning for discovering pathological continuum of crypts and evaluating therapeutic effects: An implication for in vivo preclinical study.
title_short	Deep learning for discovering pathological continuum of crypts and evaluating therapeutic effects: An implication for in vivo preclinical study.
title_full	Deep learning for discovering pathological continuum of crypts and evaluating therapeutic effects: An implication for in vivo preclinical study.
title_fullStr	Deep learning for discovering pathological continuum of crypts and evaluating therapeutic effects: An implication for in vivo preclinical study.
title_full_unstemmed	Deep learning for discovering pathological continuum of crypts and evaluating therapeutic effects: An implication for in vivo preclinical study.
title_sort	deep learning for discovering pathological continuum of crypts and evaluating therapeutic effects: an implication for in vivo preclinical study.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/9792d8cf3d574844bf4b1922ca50e9e1
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Deep learning for discovering pathological continuum of crypts and evaluating therapeutic effects: An implication for in vivo preclinical study.

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