Generalising from conventional pipelines using deep learning in high‑throughput screening workfows

The study of complex diseases relies on large amounts of data to build models toward precision
medicine. Such data acquisition is feasible in the context of high-throughput screening, in which
the quality of the results relies on the accuracy of the image analysis. Although state-of-the-art
solutions for image segmentation employ deep learning approaches, the high cost of manually
generating ground truth labels for model training hampers the day-to-day application in experimental
laboratories. Alternatively, traditional computer vision-based solutions do not need expensive
labels for their implementation. Our work combines both approaches by training a deep learning
network using weak training labels automatically generated with conventional computer vision
methods. Our network surpasses the conventional segmentation quality by generalising beyond
noisy labels, providing a 25% increase of mean intersection over union, and simultaneously reducing
the development and inference times. Our solution was embedded into an easy-to-use graphical
user interface that allows researchers to assess the predictions and correct potential inaccuracies
with minimal human input. To demonstrate the feasibility of training a deep learning solution on a
large dataset of noisy labels automatically generated by a conventional pipeline, we compared our
solution against the common approach of training a model from a small manually curated dataset
by several experts. Our work suggests that humans perform better in context interpretation, such as
error assessment, while computers outperform in pixel-by-pixel fne segmentation. Such pipelines are
illustrated with a case study on image segmentation for autophagy events. This work aims for better
translation of new technologies to real-world settings in microscopy-image analysis.