A Deep Learning Pipeline for Nucleus Segmentation.

ABSTRACT: Deep learning is rapidly becoming the technique of choice for automated segmentation of nuclei in biological image analysis workflows. In order to evaluate the feasibility of training nuclear segmentation models on small, custom annotated image datasets that have been augmented, we have designed a computational pipeline to systematically compare different nuclear segmentation model architectures and model training strategies. Using this approach, we demonstrate that transfer learning and tuning of training parameters, such as the composition, size, and preprocessing of the training image dataset, can lead to robust nuclear segmentation models, which match, and often exceed, the performance of existing, off-the-shelf deep learning models pretrained on large image datasets. We envision a pra

SUBMITTER: Zaki G 

PROVIDER: S-EPMC8914348 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature