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Automated Morphological Profiling via Deep Learning-Based Segmentation for High-Throughput Phenotypic Screening.

Bendegúz H Zováthi¹, Philipp Kainz²

¹Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, 1083 Budapest, Hungary.

Journal of Imaging

|April 27, 2026

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A new deep learning workflow automates Cell Painting analysis for drug discovery, significantly reducing manual effort and improving efficiency. This segmentation-driven approach enhances compound evaluation and accessibility in morphological profiling.

Area of Science:

Computational Biology
Drug Discovery
Bioimage Analysis

Background:

Morphological profiling using Cell Painting is crucial for compound evaluation in drug discovery.
Existing CellProfiler pipelines require significant manual configuration and technical expertise, limiting scalability.

Purpose of the Study:

To develop a fully automated, deep learning-based workflow for segmentation-driven morphological profiling.
To reduce the configuration overhead and technical barriers associated with Cell Painting analysis.

Main Methods:

A U-net-based segmentation model was trained on the JUMP Cell Painting dataset using ground-truth masks.
Post-processing strategies were implemented to enhance instance separation and minimize segmentation artifacts.
The automated workflow was validated against established CellProfiler measurements.

Keywords:

Cell Painting artificial intelligence deep learning segmentation drug discovery high-content screening high-throughput imaging instance segmentation microscopic image analysis morphological profiling phenotypic screening

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Main Results:

The deep learning model achieved high segmentation performance (e.g., AP up to 0.92 for nuclei) with rapid processing times (2.2 s per image).
The workflow extracted 3664 morphological descriptors highly correlated with CellProfiler data (normalized MAE: 0.0298).
Feature prioritization reduced descriptors to 1145 informative features, minimizing redundancy.

Conclusions:

Automated deep learning pipelines can effectively complement traditional Cell Painting workflows.
The proposed method enhances resource efficiency and accessibility in drug discovery and personalized medicine.
This approach maintains compatibility with established morphological profiling standards while reducing manual effort.