Chen Alain, Han Shuo, Lee Soonam, Fu Chichen, Yang Changye, Wu Liming, Winfree Seth, Dunn Kenneth W, Salama Paul, Delp Edward J
Purdue University, School of Electrical and Computer Engineering, Video and Image Processing Laboratory, West Lafayette, Indiana, United States.
National Institute of Allergy and Infectious Diseases, Rocky Mountain Laboratories, Hamilton, Montana, United States.
J Med Imaging (Bellingham). 2025 Mar;12(2):024001. doi: 10.1117/1.JMI.12.2.024001. Epub 2025 Mar 11.
The advancement of high-content optical microscopy has enabled the acquisition of very large three-dimensional (3D) image datasets. The analysis of these image volumes requires more computational resources than a biologist may have access to in typical desktop or laptop computers. This is especially true if machine learning tools are being used for image analysis. With the increased amount of data analysis and computational complexity, there is a need for a more accessible, easy-to-use, and efficient network-based 3D image processing system. The distributed and networked analysis of volumetric image data (DINAVID) system was developed to enable remote analysis of 3D microscopy images for biologists.
We present an overview of the DINAVID system and compare it to other tools currently available for microscopy image analysis. DINAVID is designed using open-source tools and has two main sub-systems, a computational system for 3D microscopy image processing and analysis and a 3D visualization system.
DINAVID is a network-based system with a simple web interface that allows biologists to upload 3D volumes for analysis and visualization. DINAVID enables the image access model of a center hosting image volumes and remote users analyzing those volumes, without the need for remote users to manage any computational resources.
The DINAVID system, designed and developed using open-source tools, enables biologists to analyze and visualize 3D microscopy volumes remotely without the need to manage computational resources. DINAVID also provides several image analysis tools, including pre-processing and several segmentation models.
高内涵光学显微镜技术的进步使得能够获取非常大的三维(3D)图像数据集。对这些图像体积进行分析所需的计算资源比生物学家在典型的台式机或笔记本电脑上所能获得的资源更多。如果使用机器学习工具进行图像分析,情况尤其如此。随着数据分析量和计算复杂度的增加,需要一个更易访问、易于使用且高效的基于网络的3D图像处理系统。开发了体积图像数据的分布式网络分析(DINAVID)系统,以使生物学家能够对3D显微镜图像进行远程分析。
我们概述了DINAVID系统,并将其与当前可用于显微镜图像分析的其他工具进行比较。DINAVID使用开源工具设计,有两个主要子系统,一个用于3D显微镜图像处理和分析的计算系统以及一个3D可视化系统。
DINAVID是一个基于网络的系统,具有简单的网页界面,允许生物学家上传3D体积数据进行分析和可视化。DINAVID实现了由一个托管图像体积数据的中心和远程分析这些体积数据的用户组成的图像访问模型,而无需远程用户管理任何计算资源。
使用开源工具设计和开发的DINAVID系统使生物学家能够远程分析和可视化3D显微镜体积数据,而无需管理计算资源。DINAVID还提供了几种图像分析工具,包括预处理和几种分割模型。