MiniVess: A dataset of rodent cerebrovasculature from in vivo multiphoton fluorescence microscopy imaging (v1)

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.

本研究提出MiniVess——首个基于双光子荧光显微镜（two-photon fluorescence microscopy）采集的啮齿动物脑血管带标注数据集。MiniVess包含70组三维图像体数据，且均配有分割真值标注。该数据集的分割标注通过传统图像处理操作、U-Net模型结合人工校对完成。本研究同时提供了图像预处理流程及U-Net模型的相关代码。监督式机器学习方法已被广泛应用于生物医学图像的自动化处理。尽管过往研究多侧重于新型网络架构与损失函数的开发，但当前学界对公开可用的带标注（或已分割）数据集的需求正持续攀升。带标注的数据集是模型训练与验证环节不可或缺的组成部分。尤为重要的是，来自不同实验室的数据集可用于检验模型的泛化能力。我们期望本数据集能够为评估用于生物医学图像分析的机器学习工具的可靠性提供助力。

搜集汇总

背景与挑战

背景概述

MiniVess是首个带注释的啮齿动物脑血管数据集，包含70个3D图像体积和分割真值，通过双光子荧光显微镜采集。该数据集专注于血管系统研究，涉及大鼠、小鼠和阿尔茨海默病模型，旨在支持机器学习工具在生物医学图像分析中的训练和验证。