A Deep Learning Approach for Improving Two-Photon Vascular Imaging Speeds.

ABSTRACT: A potential method for tracking neurovascular disease progression over time in preclinical models is multiphoton fluorescence microscopy (MPM), which can image cerebral vasculature with capillary-level resolution. However, obtaining high-quality, three-dimensional images with traditional point scanning MPM is time-consuming and limits sample sizes for chronic studies. Here, we present a convolutional neural network-based (PSSR Res-U-Net architecture) algorithm for fast upscaling of low-resolution or sparsely sampled images and combine it with a segmentation-less vectorization process for 3D reconstruction and statistical analysis of vascular network structure. In doing so, we also demonstrate that the use of semi-synthetic training data can replace the expensive and arduous process of acqu

SUBMITTER: Zhou A 

PROVIDER: S-EPMC10886311 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature