![]() An open-source alternative is FIJI (Fiji Is Just ImageJ) 20. In practice, substantial manual modifications are required. Furthermore, while automated modules produce impressive results for images with high SNR, biomedical images, particularly intravital 2PFM images, are inherently noisy. Comprehensive analyses of datasets are therefore functionally limited by the modules available and can become prohibitively expensive. Each analysis type, such as vascular segmentation and cell tracking, is generally sold as separate modules. Most methods of automated image processing of 2PFM images rely on proprietary software, such as Imaris (Bitplane, United Kingdom) and Volocity (Quorum Technologies, Canada). 2PFM has been extensively used to investigate various phenomena, including neural activity using voltage-sensitive dyes 16and calcium indicators 17, microglial activity using transgenic animal models 18, and vascular dynamics 19. ![]() The use of longer wavelengths also results in less scattering by the neural tissue, allowing imaging at deeper depths within the brain. While the resolution of 2PFM can be on par with confocal microscopy, the risk of phototoxicity and photobleaching of tissues and fluorophores is substantially reduced because the excitation volume is limited to the focal volume of the microscope 15. In the neurosciences, two-photon fluorescence microscopy (2PFM) is currently the technique of choice for intravital microscopy. Thus, segmentation of blood vessels is a necessary preprocessing step that facilitates further vascular and cellular analyses. Similar distance metrics can be used to analyze pathological entities, such as perivascular Aβ plaques 13 and atherosclerotic plaques 14. For example, recruitment of peripheral leukocytes to cerebrovasculature has been observed following traumatic brain injury 10, middle cerebral artery occlusion 11, and in Alzheimer’s disease 12. Vascular-cellular interactions have been of particular interest in studies focused on diseases. Such cells and their interactions with vasculature can be identified and analyzed based on distance metrics to vascular walls, a task which is simplified with accurate vascular segmentation masks. In addition to the endothelial and mural cells that make up the blood vessel proper, various other cell types interact with vascular walls, including astrocyte endfeet processes, perivascular macrophages, and peripheral leukocytes. Structural characteristics can also be used as predictors or markers to assist in the diagnosis of diseases, such as Alzheimer’s disease 3, 4, traumatic brain injury 5, brain tumours 6, atherosclerosis 7, and retinal pathology 8, 9.Īpart from vascular analyses, blood vessel segmentation is also a necessary preprocessing step for the analysis of cells and pathological entities (Fig. For example, in ischemic stroke studies, vascular segmentation enables detection and quantification of vascular occlusions, which can be helpful in determining therapeutic options 1, 2. Blood vessel segmentation has clear clinical value. Identification of blood vessels as arterioles, venules, or capillaries can be used to analyze vascular dynamics, such as blood flow and vascular supply. By creating a segmentation, or a mask, that separates vascular from non-vascular pixels, structural information about the vascular system can be acquired, such as diameter, branch order, and blood vessel type. We hope this dataset will be helpful in testing the reliability of machine learning tools for analyzing biomedical images.īlood vessel segmentation is often a necessary prerequisite for extracting meaningful analyses from biomedical imaging data. In particular, datasets that are collected from different labs are necessary to test the generalizability of models. Annotated datasets are necessary during model training and validation. While much emphasis has been placed on the development of new network architectures and loss functions, there has been an increased emphasis on the need for publicly available annotated, or segmented, datasets. Supervised machine learning methods have been widely used for automated image processing of biomedical images. Code for image preprocessing steps and the U-Net are provided. Segmentations were created using traditional image processing operations, a U-Net, and manual proofreading. MiniVess consists of 70 3D image volumes with segmented ground truths. We present MiniVess, the first annotated dataset of rodent cerebrovasculature, acquired using two-photon fluorescence microscopy.
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