A convolutional neural network for segmentation of yeast cells without manual training annotations.

ABSTRACT: Single-cell time-lapse microscopy is a ubiquitous tool for studying the dynamics of complex cellular processes. While imaging can be automated to generate very large volumes of data, the processing of the resulting movies to extract high-quality single-cell information remains a challenging task. The development of software tools that automatically identify and track cells is essential for realizing the full potential of time-lapse microscopy data. Convolutional neural networks (CNNs) are ideally suited for such applications, but require great amounts of manually annotated data for training, a time-consuming and tedious process. We developed a new approach to CNN training for yeast cell segmentation based on synthetic data, and present i) a software tool for the generation of synthetic ima

SUBMITTER: Kruitbosch H 

PROVIDER: S-EPMC8825468 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature