Application of convolutional neural networks towards nuclei segmentation in localization-based super-resolution fluorescence microscopy images.

ABSTRACT:

Background

Automated segmentation of nuclei in microscopic images has been conducted to enhance throughput in pathological diagnostics and biological research. Segmentation accuracy and speed has been significantly enhanced with the advent of convolutional neural networks. A barrier in the broad application of neural networks to nuclei segmentation is the necessity to train the network using a set of application specific images and image labels. Previous works have attempted to create broadly trained networks for universal nuclei segmentation; however, such networks do not work on all imaging modalities, and best results are still commonly found when the network is retrained on user specific data. Stochastic optical reconstruction microscopy (STORM) based super-resolution fluoresce

SUBMITTER: Mela CA 

PROVIDER: S-EPMC8204587 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature