Generalising from Conventional Pipelines: A Case Study in Deep Learning-Based for High-Throughput Screening

Abstract

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 fine 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.

Systematic Transmission Electron Microscopy‐Based Identification and 3D Reconstruction of Cellular Degradation Machinery

“…Deep learning techniques are a promising area for the study of autophagy, allowing for automated data quantification based on machine learning techniques. [88,89] In general, this technique allows for high-throughput methods that may be necessary for larger data sets. However, many deep learning techniques for autophagy are reliant on fluorescence, which offers less precision and flexibility in measurements than the method presented here.…”