Interactive machine learning for fast and robust cell profiling

Abstract

These authors contributed equally to this work. AbstractProfiling 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.

Mātauranga Māori and anti-microbials: Searching for new tools to control the spread of Kauri Dieback

“…For viability staining, oospores were first stained with 10 μL of (20x) (FDA) and incubated at 37°C for 20 h. Then, 10 μL of 20 µM TOTO-3 iodide was added to sample and incubated for a further 4 h at 37 °C. The images were analyzed and oospores counted using CellProfiler v3.1.8 (Laux et al, 2020).…”