SAMPLE-BASED TRAINING DATA FOR EFFECTIVE 3D CELL SEGMENTATION

Smith, A.; Bretschneider, T.

doi:10.64898/2025.12.05.692330

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bioRxiv · 10.64898/2025.12.05.692330

SAMPLE-BASED TRAINING DATA FOR EFFECTIVE 3D CELL SEGMENTATION

Smith, A.·Bretschneider, T.

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Abstract

Deep learning remains the leading choice for cell segmentation, a critical step in bioimaging analysis. Whilst deep learning models provide excellent segmentation accuracy, a large number of hand-annotated samples are required which are scarce in 3D. We propose a method for generating synthetic data for training deep learning models to augment or replace such datasets. By preserving key features in real data, we show that a standard UNet trained on synthetic data can segment single motile cells with branching filopodia with high accuracy. We also demonstrate how low-effort slice annotations can sufficiently replace volume annotations in our data generation pipeline. Overall, we provide a simple alternative to annotating large 3D datasets for training neural networks to segment cell imaging data.

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Smith, A., Bretschneider, T.. 2025-12-09. SAMPLE-BASED TRAINING DATA FOR EFFECTIVE 3D CELL SEGMENTATION. https://doi.org/10.64898/2025.12.05.692330

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