Mathematical imaging methods for mitosis analysis in live-cell phase contrast microscopy

In this paper we propose a workflow to detect and track mitotic cells in time-lapse microscopy image sequences. In order to avoid the requirement for cell lines expressing fluorescent markers and the associated phototoxicity, phase contrast microscopy is often preferred over fluorescence microscopy in live-cell imaging. However, common specific image characteristics complicate image processing and impede use of standard methods. Nevertheless, automated analysis is desirable due to manual analysis being subjective, biased and extremely time-consuming for large data sets. Here, we present the following workflow based on mathematical imaging methods. In the first step, mitosis detection is performed by means of the circular Hough transform. The obtained circular contour subsequently serves as an initialisation for the tracking algorithm based on variational methods. It is sub-divided into two parts: in order to determine the beginning of the whole mitosis cycle, a backwards tracking procedure is performed. After that, the cell is tracked forwards in time until the end of mitosis. As a result, the average of mitosis duration and ratios of different cell fates (cell death, no division, division into two or more daughter cells) can be measured and statistics on cell morphologies can be obtained. All of the tools are featured in the user-friendly MATLAB®Graphical User Interface MitosisAnalyser.JSG acknowledges support by the NIHR Cambridge Biomedical Research Centre and would like to thank Hendrik Dirks, Fjedor Gaede [45] and Jonas Geiping [46] for fruitful discussions in the context of a practical course at WWU Münster in 2014 and significant speed-up and GPU implementation of earlier versions of the code. JSG and MB would like to thank Michael Möller for providing the basic tracking code and acknowledge support by ERC via Grant EU FP 7 - ERC Consolidator Grant 615216 LifeInverse. MB acknowledges further support by the German Science Foundation DFG via Cells-in-Motion Cluster of Excellence. CBS acknowledges support from the EPSRC grant Nr. EP/M00483X/1, from the Leverhulme grant “Breaking the non-convexity barrier”, from the EPSRC Centre for Mathematical And Statistical Analysis Of Multimodal Clinical Imaging grant Nr. EP/N014588/1, and the Cantab Capital Institute for the Mathematics of Information. JAH, SBK, JAP, AS and SR were funded by Cancer Research UK, The University of Cambridge and Hutchison Whampoa Ltd. SBK also received funding from Pancreatic Cancer UK

Research Data Supporting "Learning Filter Functions in Regularisers by Minimising Quotients"

This data contains the code and images necessary to reproduce the computational results published in "Learning Filter Functions in Regularisers by Minimising Quotients"