Good Cell, Bad Cell: Classification of Segmented Images for Suitable Quantification and Analysis
نویسندگان
چکیده
While open-source tools exist to automatically segment and track cells in time-lapse microscopy experiments, the resulting output must be inspected to detect and remove spurious artifacts. In addition, data not exhibiting proper experimental controls must be discarded before performing downstream analysis. Currently, the data set is manually examined to accomplish these tasks. Here we present machine learning approaches for classifying the suitability of data traces for downstream processing and analysis. We integrate the most successful approaches into the experimental workflow.
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