Spectroscopic and deep learning-based approaches to identify and quantify cerebral microhemorrhages

Crouzet, Christian; Jeong, Gwangjin; Chae, Rachel; LoPresti, Krystal; Dunn, Cody E.; Xie, Danny; Agu, Chiagoziem; Fang, Chuo; Nunes, Ane Cláudia Fernandes; Lau, Wei Ling; Kim, Sehwan; Cribbs, David H.; Fisher, Mark; Choi, Bernard

doi:10.1038/s41598-021-88236-1

Cited by 3 publications

(3 citation statements)

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“…We previously demonstrated the effectiveness of iDISCO in combination with lectin-DyLight-649 for 3D visualization of vasculature in a mouse brain [13]. We also used Prussian blue labeling of hemosiderin [14][15][16], a by-product of cerebral microhemorrhages, with iDISCO-cleared brains and lectin-DyLight-649 labeling.…”

Section: Introductionmentioning

confidence: 99%

Semi-automated protocol to quantify and characterize fluorescent three-dimensional vascular images

Xie,

Crouzet,

LoPresti

et al. 2024

PLoS ONE

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The microvasculature facilitates gas exchange, provides nutrients to cells, and regulates blood flow in response to stimuli. Vascular abnormalities are an indicator of pathology for various conditions, such as compromised vessel integrity in small vessel disease and angiogenesis in tumors. Traditional immunohistochemistry enables the visualization of tissue cross-sections containing exogenously labeled vasculature. Although this approach can be utilized to quantify vascular changes within small fields of view, it is not a practical way to study the vasculature on the scale of whole organs. Three-dimensional (3D) imaging presents a more appropriate method to visualize the vascular architecture in tissue. Here we describe the complete protocol that we use to characterize the vasculature of different organs in mice encompassing the methods to fluorescently label vessels, optically clear tissue, collect 3D vascular images, and quantify these vascular images with a semi-automated approach. To validate the automated segmentation of vascular images, one user manually segmented one hundred random regions of interest across different vascular images. The automated segmentation results had an average sensitivity of 83±11% and an average specificity of 91±6% when compared to manual segmentation. Applying this procedure of image analysis presents a method to reliably quantify and characterize vascular networks in a timely fashion. This procedure is also applicable to other methods of tissue clearing and vascular labels that generate 3D images of microvasculature.

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Section: Introductionmentioning

confidence: 99%

Semi-automated protocol to quantify and characterize fluorescent three-dimensional vascular images

Xie,

Crouzet,

LoPresti

et al. 2024

PLoS ONE

Self Cite

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show abstract

“…We previously demonstrated the effectiveness of iDISCO in combination with lectin-DyLight-649 for 3D visualization of vasculature in a mouse brain (Khouri et al, 2021). We also have used Prussian blue labelling of hemosiderin (Crouzet et al, 2021; Lo et al, 2016; Sumbria et al, 2018), a by-product of cerebral microhemorrhages, with iDISCO-cleared brains and lectin-DyLight-649 labelling.…”

Section: Introductionmentioning

confidence: 99%

“…We also have used Prussian blue labelling of hemosiderin (11)(12)(13), a by-product of cerebral microhemorrhages, with iDISCO-cleared brains and lectin-DyLight-649 labelling.…”

Section: Introductionmentioning

confidence: 99%

Semi-automated protocol to quantify and characterize fluorescent three-dimensional vascular images

Xie

Crouzet

LoPresti

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

The microvasculature facilitates gas exchange, provides nutrients to cells, and regulates blood flow in response to stimuli. Vascular abnormalities are an indicator of pathology for various conditions, such as compromised vessel integrity in small vessel disease and angiogenesis in tumors. Traditional immunohistochemistry enables visualization of tissue cross-sections containing exogenously labeled vasculature. Although this approach can be utilized to quantify vascular changes within small fields-of-view, it is not a practical way to study the vasculature on the scale of whole organs. Three-dimensional (3D) imaging presents a more appropriate method to visualize the vascular architecture in tissue. Here we describe the complete protocol that we use to characterize the vasculature of different organs in mice encompassing the methods to fluorescently label vessels, optically clear tissue, collect 3D vascular images, and quantify these vascular images with a semi-automated approach. To validate the automated segmentation of vascular images, one user manually segmented fifty random regions of interest across different vascular images. The automated segmentation results had an average sensitivity of 80 +/-8% and an average specificity of 90 +/-5% when compared to manual segmentation. Applying this procedure of image analysis presents a method to reliably quantify and characterize vascular networks in a timely fashion. This procedure is also applicable to other methods of tissue clearing and vascular labels that generate 3D images of microvasculature.

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A Novel Detection and Classification Framework for Diagnosing of Cerebral Microbleeds Using Transformer and Language

Chen,

Zhao,

Lang

et al. 2024

Bioengineering

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The detection of Cerebral Microbleeds (CMBs) is crucial for diagnosing cerebral small vessel disease. However, due to the small size and subtle appearance of CMBs in susceptibility-weighted imaging (SWI), manual detection is both time-consuming and labor-intensive. Meanwhile, the presence of similar-looking features in SWI images demands significant expertise from clinicians, further complicating this process. Recently, there has been a significant advancement in automated detection of CMBs using a Convolutional Neural Network (CNN) structure, aiming at enhancing diagnostic efficiency for neurologists. However, existing methods still show discrepancies when compared to the actual clinical diagnostic process. To bridge this gap, we introduce a novel multimodal detection and classification framework for CMBs’ diagnosis, termed MM-UniCMBs. This framework includes a light-weight detection model and a multi-modal classification network. Specifically, we proposed a new CMBs detection network, CMBs-YOLO, designed to capture the salient features of CMBs in SWI images. Additionally, we design an innovative language–vision classification network, CMBsFormer (CF), which integrates patient textual descriptions—such as gender, age, and medical history—with image data. The MM-UniCMBs framework is designed to closely align with the diagnostic workflow of clinicians, offering greater interpretability and flexibility compared to existing methods. Extensive experimental results show that MM-UniCMBs achieves a sensitivity of 94% in CMBs’ classification and can process a patient’s data within 5 s.

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Spectroscopic and deep learning-based approaches to identify and quantify cerebral microhemorrhages

Cited by 3 publications

References 50 publications

Semi-automated protocol to quantify and characterize fluorescent three-dimensional vascular images

Semi-automated protocol to quantify and characterize fluorescent three-dimensional vascular images

Semi-automated protocol to quantify and characterize fluorescent three-dimensional vascular images

A Novel Detection and Classification Framework for Diagnosing of Cerebral Microbleeds Using Transformer and Language

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