Time-lapse microscopy is a powerful technique that generates large volumes of image-based information to quantify the behaviors of cell populations. This method has been applied to cancer studies to estimate the drug response for precision medicine and has great potential to address inter-patient (or intertumoral) heterogeneity. A couple of algorithms exist to analyze time-lapse microscopy images; however, most deal with very high-resolution images involving few cells (typically cell lines). There are currently no advanced and efficient computational frameworks available to process large-scale time-lapse microscopy imaging data to estimate patient-specific response to therapy based on a large population of primary cells. In this paper, we propose a robust and user-friendly pipeline to preprocess the images and track the behaviors of thousands of cancer cells simultaneously for a better drug response prediction of cancer patients. Availability and Implementation Source code is available at: <https://github.com/CompbioLabUCF/CellTrack> ACM Reference Format Qibing Jiang, Praneeth Sudalagunta, Mark B. Meads, Khandakar Tanvir Ahmed, Tara Rutkowski, Ken Shain, Ariosto S. Silva, and Wei Zhang. 2020. An Advanced Framework for Time-lapse Microscopy Image Analysis. In Proceedings of BioKDD: 19th International Workshop on Data Mining In Bioinformatics (BioKDD). ACM, New York, NY, USA, 8 pages. <https://doi.org/10.1145/nnnnnnn.nnnnnnn> ### Competing Interest Statement The authors have declared no competing interest.
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