ArticleBiomedical optics express2023
Hybrid machine-learning framework for volumetric segmentation and quantification of vacuoles in individual yeast cells using holotomography.
Article in Biomedical optics express, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Peering Inside the Black Box: Explainable AI to Interpret Advanced Computer Vision Fungal Pathogen Prediction.Scientific reports · 2026Article
- High-resolution assessment of multidimensional cellular mechanics using label-free refractive-index traction force microscopy.Communications biology · 2024Article
- Volumetric segmentation of biological cells and subcellular structures for optical diffraction tomography images.Biomedical optics express · 2023Article
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Authors and funding
5 authors.
Funding
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Abstract
The precise, quantitative evaluation of intracellular organelles in three-dimensional (3D) imaging data poses a significant challenge due to the inherent constraints of traditional microscopy techniques, the requirements of the use of exogenous labeling agents, and existing computational methods. To counter these challenges, we present a hybrid machine-learning framework exploiting correlative imaging of 3D quantitative phase imaging with 3D fluorescence imaging of labeled cells. The algorithm, which synergistically integrates a random-forest classifier with a deep neural network, is trained using the correlative imaging data set, and the trained network is then applied to 3D quantitative phase imaging of cell data. We applied this method to live budding yeast cells. The results revealed precise segmentation of vacuoles inside individual yeast cells, and also provided quantitative evaluations of biophysical parameters, including volumes, concentration, and dry masses of automatically segmented vacuoles.
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Registered trials
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