Deep learning-based neuron segmentation in quantitative phase images trained on procedurally generated data

Eddie M. Gil; Anna V. Sedelnikova; Zachary Steelman; Joel N. Bixler

doi:10.1117/12.2650212

16 March 2023 Deep learning-based neuron segmentation in quantitative phase images trained on procedurally generated data

Eddie M. Gil, Anna V. Sedelnikova, Zachary Steelman, Joel N. Bixler

Author Affiliations +

Proceedings Volume 12389, Quantitative Phase Imaging IX; 1238903 (2023) https://doi.org/10.1117/12.2650212
Event: SPIE BiOS, 2023, San Francisco, California, United States

Abstract

Morphological changes in neurons can denote cell health, growth, and death in response to environmental stressors. Quantitative phase imaging (QPI) has been used to assess neuronal network mass over time, which reveals such changes. High quality segmentation of cells in QPI is necessary to extract dry mass effectively. Neural networks are effective at segmentation but require vast amounts of data to train. Previously, we trained neural networks to segment neurons using simulated images that were generated from a biological neuron growth model. Images were simulated by approximating cell bodies as ellipsoids, and neurites as thin rectangular regions. The simplicity of the neuron images limited the quality of segmentation especially around neurites, which exhibit weak phase signals. In this work, improved segmentation quality is demonstrated by increasing the amount of complexity in the simulation. Namely, a data set of 5000 training images is procedurally generated by cropping cells from a sample of ten images. Cells are randomly placed, scaled and rotated into scenes of random noise and of background generated by our microscope. After training the network, its performance is tested on 100 images independent of the training data. This resulted in improvement in the Dice coefficient between the network output and the ground truth when compared with the best performing model.

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Citation Download Citation

Eddie M. Gil, Anna V. Sedelnikova, Zachary Steelman, and Joel N. Bixler "Deep learning-based neuron segmentation in quantitative phase images trained on procedurally generated data", Proc. SPIE 12389, Quantitative Phase Imaging IX, 1238903 (16 March 2023); https://doi.org/10.1117/12.2650212

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