ArticleBioinformatics (Oxford, England)2019
Evaluation of methods for generative modeling of cell and nuclear shape.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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26 citing papers in PubMed.
- Integrative Machine-Learning Molecular Subtyping and Risk Scores of Circadian Rhythm-Related Prognostic Signatures in Colorectal Cancer.Journal of gastrointestinal cancer · 2026Article
- Integration and harmonization of cell shape images for generative modeling.bioRxiv : the preprint server for biology · 2026Article
- SPRM: spatial process and relationship modeling for multiplexed images.Bioinformatics advances · 2026Article
- CytoSpatio: Learning cell type spatial relationships using multirange, multitype point process models.PLoS computational biology · 2025Article
- Clustering cell nuclei on microgrooves for disease diagnosis using deep learning.Scientific reports · 2025Article
- Interpretable representation learning for 3D multi-piece intracellular structures using point clouds.Nature methods · 2025Article
- An effective method for quantification, visualization, and analysis of 3D cell shape during early embryogenesis.Quantitative biology (Beijing, China) · 2025Article
- Automatic detection of adenoid hypertrophy on lateral nasopharyngeal radiographs of children based on deep learning.Translational pediatrics · 2024Article
- Morphological profiling for drug discovery in the era of deep learning.Briefings in bioinformatics · 2024Review
- Morphological entropy encodes cellular migration strategies on multiple length scales.NPJ systems biology and applications · 2024Article
- Orientation-invariant autoencoders learn robust representations for shape profiling of cells and organelles.Nature communications · 2024Article
- Spherical harmonics analysis reveals cell shape-fate relationships in zebrafish lateral line neuromasts.Development (Cambridge, England) · 2024Article
- Learning Morphological, Spatial, and Dynamic Models of Cellular Components.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Cellular harmonics for the morphology-invariant analysis of molecular organization at the cell surface.Nature computational science · 2023Article
- Cell shape characterization, alignment, and comparison using FlowShape.Bioinformatics (Oxford, England) · 2023Article
- Evaluation of cell segmentation methods without reference segmentations.Molecular biology of the cell · 2023Article
- Article
- Using Optimal Transport to Improve Spherical Harmonic Quantification of Complex Biological Shapes.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2022Article
- Improving and evaluating deep learning models of cellular organization.Bioinformatics (Oxford, England) · 2022Article
- MIRIAM: A machine and deep learning single-cell segmentation and quantification pipeline for multi-dimensional tissue images.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2022Article
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2 authors.
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Abstract
motivationCell shape provides both geometry for, and a reflection of, cell function. Numerous methods for describing and modeling cell shape have been described, but previous evaluation of these methods in terms of the accuracy of generative models has been limited.
resultsHere we compare traditional methods and deep autoencoders to build generative models for cell shapes in terms of the accuracy with which shapes can be reconstructed from models. We evaluated the methods on different collections of 2D and 3D cell images, and found that none of the methods gave accurate reconstructions using low dimensional encodings. As expected, much higher accuracies were observed using high dimensional encodings, with outline-based methods significantly outperforming image-based autoencoders. The latter tended to encode all cells as having smooth shapes, even for high dimensions. For complex 3D cell shapes, we developed a significant improvement of a method based on the spherical harmonic transform that performs significantly better than other methods. We obtained similar results for the joint modeling of cell and nuclear shape. Finally, we evaluated the modeling of shape dynamics by interpolation in the shape space. We found that our modified method provided lower deformation energies along linear interpolation paths than other methods. This allows practical shape evolution in high dimensional shape spaces. We conclude that our improved spherical harmonic based methods are preferable for cell and nuclear shape modeling, providing better representations, higher computational efficiency and requiring fewer training images than deep learning methods. AVAILABILITY AND IMPLEMENTATION: All software and data is available at http://murphylab.cbd.cmu.edu/software. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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