Generalising from conventional pipelines using deep learning in high-throughput screening workflows.
Sci Rep
; 12(1): 11465, 2022 07 06.
Article
in En
| MEDLINE
| ID: mdl-35794231
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
High-Throughput Screening Assays
/
Deep Learning
Type of study:
Diagnostic_studies
/
Prognostic_studies
/
Screening_studies
Limits:
Humans
Language:
En
Journal:
Sci Rep
Year:
2022
Document type:
Article
Affiliation country:
Luxemburgo