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<p>The estimation of full-field displacement between biological images or in videos is important for quantitative analyses of motion, dynamics and biophysics. However, the often-weak signals, poor contrast and a multitude of noise processes typical to microscopy make this a formidable challenge for many contemporary methods. Here, we present a deep-learning method, termed Displacement Estimation FOR Microscopy (DEFORM-Net), that outperforms traditional digital image correlation and optical flow methods, as well as recent learned approaches, offering simultaneous high accuracy, spatial sampling and speed. DEFORM-Net is experimentally unsupervised, relying on displacement simulation based on a random fractal Perlin-noise process and optimised training loss functions, without experimental ground truths. We demonstrate DEFORM-Net on real biological videos of beating neonatal mouse cardiomyocytes and pulsed contractions in <i>Drosophila</i> pupae, and in various microscopy modalities. We provide DEFORM-Net as open source, including inference in the ImageJ/FIJI platform, to empower new quantitative applications in biology and medicine.</p>

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Estimating full-field displacement in biological images using deep learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月4日

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  • Solomon J. E. T. Warsop,
  • Soraya Caixeiro,
  • Marcus Bischoff,
  • Jochen Kursawe,
  • Graham D. Bruce,
  • Philip Wijesinghe

摘要

The estimation of full-field displacement between biological images or in videos is important for quantitative analyses of motion, dynamics and biophysics. However, the often-weak signals, poor contrast and a multitude of noise processes typical to microscopy make this a formidable challenge for many contemporary methods. Here, we present a deep-learning method, termed Displacement Estimation FOR Microscopy (DEFORM-Net), that outperforms traditional digital image correlation and optical flow methods, as well as recent learned approaches, offering simultaneous high accuracy, spatial sampling and speed. DEFORM-Net is experimentally unsupervised, relying on displacement simulation based on a random fractal Perlin-noise process and optimised training loss functions, without experimental ground truths. We demonstrate DEFORM-Net on real biological videos of beating neonatal mouse cardiomyocytes and pulsed contractions in Drosophila pupae, and in various microscopy modalities. We provide DEFORM-Net as open source, including inference in the ImageJ/FIJI platform, to empower new quantitative applications in biology and medicine.