ArticleScientific data2023
A dataset of rodent cerebrovasculature from in vivo multiphoton fluorescence microscopy imaging.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Article
- Descattering and image restoration with a transformer-based neural network in deep tissue imaging.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- A new dataset for measuring the performance of blood vessel segmentation methods under distribution shifts.PloS one · 2025Article
- Deep learning-based vessel extraction in 3D confocal microscope images of cleared human glioma tissues.Biomedical optics express · 2024Article
- Fluorescent Neuronal Cells v2: multi-task, multi-format annotations for deep learning in microscopy.Scientific data · 2024Article
- A dataset of rodent cerebrovasculature from in vivo multiphoton fluorescence microscopy imaging.Scientific data · 2023Article
- U-Net based vessel segmentation for murine brains with small micro-magnetic resonance imaging reference datasets.PloS one · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
We present MiniVess, the first annotated dataset of rodent cerebrovasculature, acquired using two-photon fluorescence microscopy. MiniVess consists of 70 3D image volumes with segmented ground truths. Segmentations were created using traditional image processing operations, a U-Net, and manual proofreading. Code for image preprocessing steps and the U-Net are provided. Supervised machine learning methods have been widely used for automated image processing of biomedical images. 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. Annotated datasets are necessary during model training and validation. In particular, datasets that are collected from different labs are necessary to test the generalizability of models. We hope this dataset will be helpful in testing the reliability of machine learning tools for analyzing biomedical images.
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