Determination of mitotic delays in 3D fluorescence microscopy images of human cells using an error-correcting finite state machine

Abstract

In high-throughput cell phenotype screens large amounts of image data are acquired. The evaluation of these microscopy images requires automated image analysis methods. Here we introduce a computational scheme to process 3D multi-cell image sequences as they are produced in large-scale RNAi experiments. We describe an approach to automatically segment, track, and classify cell nuclei into seven different mitotic phases. In particular, we present an algorithm based on a finite state machine to check the consistency of the resulting sequence of mitotic phases and to correct classification errors. Our approach enables automated determination of the duration of the single phases and thus the identification of cell cultures with delayed mitotic progression.