Ensemble of Deep Learning Cascades for Segmentation of Blood Vessels in Confocal Microscopy Images

Wang, Yang Yang; Glinskii, Olga V.; Bunyak, Filiz; Palaniappan, Kannappan

doi:10.1109/aipr52630.2021.9762193

Cited by 2 publications

(3 citation statements)

References 19 publications

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“…4 Additionally, a deep learning technique to segment vasculature on confocal imaging has also been published. 25 In terms of end to end vessel structure analysis, semi automated analysis is possible through various imageJ plugins such as Vasometrics 17 , or using open sourced packages such as AngioQuant 26 , RAVE 11 , AngioTool 27 , Zebrafish Vascular Quantification (ZVQ) 19 and REAVER. 18 REAVER and ZVQ are the most recent comprehensive vessel analysis tools.…”

Section: Discussionmentioning

confidence: 99%

“…Deep learning has been successfully used to segment similar images in the past 25 , and on a dataset by dataset basis the accuracy metrics are higher than what’s achievable with a traditional image processing algorithm. However deep learning models tend to be dataset-specific and generalization to a broader set of data may require additional processing and training.…”

Section: Discussionmentioning

confidence: 99%

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Vessel Metrics: A python based software tool for automated analysis of vascular structure in confocal imaging

McGarry

Adjekukor

Ahuja

et al. 2022

Preprint

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Images contain a wealth of information that is often under analyzed in biological studies. Developmental models of vascular disease are a powerful way to quantify developmentally regulated vessel phenotypes to identify the roots of the disease process. We present vessel Metrics, a software tool specifically designed to analyze developmental vascular microscopy images that will expedite the analysis of vascular images and provide consistency between research groups. We developed a segmentation algorithm that robustly quantifies different image types, developmental stages, organisms, and disease models at a similar accuracy level to a human observer. We validate the algorithm on confocal, lightsheet, and two photon microscopy data in zebrafish. The tool accurately segments data taken by multiple scientists on varying microscopes. We validate vascular parameters such as vessel density, network length, and diameter, across developmental stages, genetic mutations, and drug treatments, and show a favorable comparison to other freely available software tools. Vessel Metrics reduces the time to analyze experimental results, improves repeatability within and between institutions, and expands the percentage of a given vascular network analyzable in experiments.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Vessel Metrics: A python based software tool for automated analysis of vascular structure in confocal imaging

McGarry

Adjekukor

Ahuja

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In addition, α-SMA immunostaining also allowed to identify vascular smooth muscle cells (VSMCs). To quantitate changes in VSMC coverage of meningeal blood vessels, first deep learning cascades were used for blood vessel segmentation as previously described [ 20 ] to determine blood vessel boundaries and calculate blood vessel network area in each microscopic image. Next, for each image, α-SMA associated immunofluorescence signal was measured over the blood vessel network area, and the ratio of α-SMA associated immunofluorescence to blood vessel network area calculated to reflect blood vessel VSMC coverage.…”

Section: Methodsmentioning

confidence: 99%

Corneal injury is associated with stromal and vascular alterations within cranial dura mater

et al. 2023

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The cornea and cranial dura mater share sensory innervation. This link raises the possibility that pathological impulses mediated by corneal injury may be transmitted to the cranial dura, trigger dural perivascular/connective tissue nociceptor responses, and induce vascular and stromal alterations affecting dura mater blood and lymphatic vessel functionality. In this study, using a mouse model, we demonstrate for the first time that two weeks after the initial insult, alkaline injury to the cornea leads to remote pathological changes within the coronal suture area of the dura mater. Specifically, we detected significant pro-fibrotic changes in the dural stroma, as well as vascular remodeling characterized by alterations in vascular smooth muscle cell (VSMC) morphology, reduced blood vessel VSMC coverage, endothelial cell expression of the fibroblast specific protein 1, and significant increase in the number of podoplanin-positive lymphatic sprouts. Intriguingly, the deficiency of a major extracellular matrix component, small leucine-rich proteoglycan decorin, modifies both the direction and the extent of these changes. As the dura mater is the most important route for the brain metabolic clearance, these results are of clinical relevance and provide a much-needed link explaining the association between ophthalmic conditions and the development of neurodegenerative diseases.

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Ensemble of Deep Learning Cascades for Segmentation of Blood Vessels in Confocal Microscopy Images

Cited by 2 publications

References 19 publications

Vessel Metrics: A python based software tool for automated analysis of vascular structure in confocal imaging

Vessel Metrics: A python based software tool for automated analysis of vascular structure in confocal imaging

Corneal injury is associated with stromal and vascular alterations within cranial dura mater

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