Iterative unsupervised domain adaptation for generalized cell detection from brightfield z-stacks.

ABSTRACT:

Background

Cell counting from cell cultures is required in multiple biological and biomedical research applications. Especially, accurate brightfield-based cell counting methods are needed for cell growth analysis. With deep learning, cells can be detected with high accuracy, but manually annotated training data is required. We propose a method for cell detection that requires annotated training data for one cell line only, and generalizes to other, unseen cell lines.

Results

Training a deep learning model with one cell line only can provide accurate detections for similar unseen cell lines (domains). However, if the new domain is very dissimilar from training domain, high precision but lower recall is achieved. Generalization capabilities of the model can be improved with t

SUBMITTER: Liimatainen K 

PROVIDER: S-EPMC6376647 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature