Fully unsupervised symmetry-based mitosis detection in time-lapse cell microscopy.

ABSTRACT:

Motivation

Cell microscopy datasets have great diversity due to variability in cell types, imaging techniques and protocols. Existing methods are either tailored to specific datasets or are based on supervised learning, which requires comprehensive manual annotations. Using the latter approach, however, poses a significant difficulty due to the imbalance between the number of mitotic cells with respect to the entire cell population in a time-lapse microscopy sequence.

Results

We present a fully unsupervised framework for both mitosis detection and mother-daughters association in fluorescence microscopy data. The proposed method accommodates the difficulty of the different cell appearances and dynamics. Addressing symmetric cell divisions, a key concept is utilizing daughters

SUBMITTER: Gilad T 

PROVIDER: S-EPMC6662301 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature