Two-step machine learning method for the rapid analysis of microvascular flow in intravital video microscopy

Abstract Microvascular blood flow is crucial for tissue and organ function and is often severely affected by diseases. Therefore, investigating the microvasculature under different pathological circumstances is essential to understand the role of the microcirculation in health and sickness. Microvas...

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Autores principales:	Ossama Mahmoud, Mahmoud El-Sakka, Barry G. H. Janssen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/34ca9b80a1b74d5db3d38ce65d1ea0c2
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spelling	oai:doaj.org-article:34ca9b80a1b74d5db3d38ce65d1ea0c22021-12-02T14:35:34ZTwo-step machine learning method for the rapid analysis of microvascular flow in intravital video microscopy10.1038/s41598-021-89469-w2045-2322https://doaj.org/article/34ca9b80a1b74d5db3d38ce65d1ea0c22021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89469-whttps://doaj.org/toc/2045-2322Abstract Microvascular blood flow is crucial for tissue and organ function and is often severely affected by diseases. Therefore, investigating the microvasculature under different pathological circumstances is essential to understand the role of the microcirculation in health and sickness. Microvascular blood flow is generally investigated with Intravital Video Microscopy (IVM), and the captured images are stored on a computer for later off-line analysis. The analysis of these images is a manual and challenging process, evaluating experiments very time consuming and susceptible to human error. Since more advanced digital cameras are used in IVM, the experimental data volume will also increase significantly. This study presents a new two-step image processing algorithm that uses a trained Convolutional Neural Network (CNN) to functionally analyze IVM microscopic images without the need for manual analysis. While the first step uses a modified vessel segmentation algorithm to extract the location of vessel-like structures, the second step uses a 3D-CNN to assess whether the vessel-like structures have blood flowing in it or not. We demonstrate that our two-step algorithm can efficiently analyze IVM image data with high accuracy (83%). To our knowledge, this is the first application of machine learning for the functional analysis of microvascular blood flow in vivo.Ossama MahmoudMahmoud El-SakkaBarry G. H. JanssenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ossama Mahmoud Mahmoud El-Sakka Barry G. H. Janssen Two-step machine learning method for the rapid analysis of microvascular flow in intravital video microscopy
description	Abstract Microvascular blood flow is crucial for tissue and organ function and is often severely affected by diseases. Therefore, investigating the microvasculature under different pathological circumstances is essential to understand the role of the microcirculation in health and sickness. Microvascular blood flow is generally investigated with Intravital Video Microscopy (IVM), and the captured images are stored on a computer for later off-line analysis. The analysis of these images is a manual and challenging process, evaluating experiments very time consuming and susceptible to human error. Since more advanced digital cameras are used in IVM, the experimental data volume will also increase significantly. This study presents a new two-step image processing algorithm that uses a trained Convolutional Neural Network (CNN) to functionally analyze IVM microscopic images without the need for manual analysis. While the first step uses a modified vessel segmentation algorithm to extract the location of vessel-like structures, the second step uses a 3D-CNN to assess whether the vessel-like structures have blood flowing in it or not. We demonstrate that our two-step algorithm can efficiently analyze IVM image data with high accuracy (83%). To our knowledge, this is the first application of machine learning for the functional analysis of microvascular blood flow in vivo.
format	article
author	Ossama Mahmoud Mahmoud El-Sakka Barry G. H. Janssen
author_facet	Ossama Mahmoud Mahmoud El-Sakka Barry G. H. Janssen
author_sort	Ossama Mahmoud
title	Two-step machine learning method for the rapid analysis of microvascular flow in intravital video microscopy
title_short	Two-step machine learning method for the rapid analysis of microvascular flow in intravital video microscopy
title_full	Two-step machine learning method for the rapid analysis of microvascular flow in intravital video microscopy
title_fullStr	Two-step machine learning method for the rapid analysis of microvascular flow in intravital video microscopy
title_full_unstemmed	Two-step machine learning method for the rapid analysis of microvascular flow in intravital video microscopy
title_sort	two-step machine learning method for the rapid analysis of microvascular flow in intravital video microscopy
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/34ca9b80a1b74d5db3d38ce65d1ea0c2
work_keys_str_mv	AT ossamamahmoud twostepmachinelearningmethodfortherapidanalysisofmicrovascularflowinintravitalvideomicroscopy AT mahmoudelsakka twostepmachinelearningmethodfortherapidanalysisofmicrovascularflowinintravitalvideomicroscopy AT barryghjanssen twostepmachinelearningmethodfortherapidanalysisofmicrovascularflowinintravitalvideomicroscopy
_version_	1718391063795728384

Two-step machine learning method for the rapid analysis of microvascular flow in intravital video microscopy

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