PT  - JOURNAL ARTICLE
AU  - Lisa Laux
AU  - Marie F.A. Cutiongco
AU  - Nikolaj Gadegaard
AU  - Bjørn Sand Jensen
TI  - Interactive machine learning for fast and robust cell profiling
AID  - 10.1101/2020.02.20.956268
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.02.20.956268
4099  - http://biorxiv.org/content/early/2020/02/20/2020.02.20.956268.short
4100  - http://biorxiv.org/content/early/2020/02/20/2020.02.20.956268.full
AB  - Profiling cell morphology is a powerful tool for inferring cell function. However, this technique retains a high barrier to entry. In particular, configuring image processing parameters for optimal cell profiling is susceptible to cognitive biases, and dependent on user experience. Here, we present an interactive machine learning strategy that learns the optimum cell profiling configuration to maximise quality of the cell profiling outcome. The process is guided by the user, from whom a rating of the quality of a cell profiling configuration is obtained. The machine learning algorithm uses this information to automatically recommend the next configuration to examine. We validated our interactive approach against the standard human trial-and-error scheme to optimise an object segmentation task on the standard software CellProfiler. Our approach enabled rapid optimisation of an object segmentation pipeline, which more accurately segmented objects compared to those optimsed through human trial-and-error. Users also attested to the ease of use and reduced cognitive load enabled by our machine learning strategy over the standard approach. We envision that our interactive machine learning strategy can enhance the quality and efficiency of pipeline optimisation to democratise image-based cell profiling.