A dataset of rodent cerebrovasculature from in vivo multiphoton fluorescence microscopy imaging

• Published in: Scientific data Vol. 10; no. 1; p. 141
• Main Authors: Poon, Charissa, Teikari, Petteri, Rachmadi, Muhammad Febrian, Skibbe, Henrik, Hynynen, Kullervo
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 17.03.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/378/1341; 639/166/985; Animals; Annotations; Automation; Blood vessels; Brain research; Business metrics; Cerebrovascular Circulation; Data Descriptor; Datasets; Deep learning; Fluorescence microscopy; Humanities and Social Sciences; Image processing; Image Processing, Computer-Assisted - methods; Imaging, Three-Dimensional; Learning algorithms; Leukocytes; Machine learning; Microscopy; Microscopy, Fluorescence, Multiphoton; multidisciplinary; Rodentia; Science; Science (multidisciplinary)
• ISSN: 2052-4463; 2052-4463
• DOI: 10.1038/s41597-023-02048-8

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.