ArticleNature communications2024
Orientation-invariant autoencoders learn robust representations for shape profiling of cells and organelles.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Deep learning reveals ecological decoupling and rate heterogeneity in Hymenoptera wing evolution.Science advances · 2026Article
- Development of a universal imaging "phenome" using shape, appearance and motion (SAM) features and the SAM Phenotype Observation Tool (SPOT).Nature communications · 2026Article
- Fast and interpretable quantification of biological shape heterogeneity via stratified Wasserstein kernel.PLoS computational biology · 2026Article
- Unsupervised Variational-Autoencoder-Based Analysis of Morphological Representations in Magnetic-Nanoparticle-Treated Macrophages.Bioengineering (Basel, Switzerland) · 2026Article
- Quantifying the Single-Cell Morphological Landscape of Cellular Transdifferentiation through Force Field Reconstruction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- The impact of pre-processing techniques on deep learning breast image segmentation.Scientific reports · 2025Article
- A deep learning approach for time-consistent cell cycle phase prediction from microscopy data.PLoS computational biology · 2025Article
- Label-Free Detection of Nuclear Envelope Nucleoporation using 2D Morphological Embeddings and Machine Learning.Small (Weinheim an der Bergstrasse, Germany) · 2025Article
- Deep Disentangled Representation Learning Reveals Neuron Subtype-Specific Nuclear Morphologies Across Aging in Mice and Humans.bioRxiv : the preprint server for biology · 2025Article
- Neurons and astrocytes have distinct organelle signatures and responses to stress.Cell reports · 2025Article
- Article
- Orientation-invariant autoencoders learn robust representations for shape profiling of cells and organelles.Nature communications · 2024Article
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6 authors.
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Abstract
Cell and organelle shape are driven by diverse genetic and environmental factors and thus accurate quantification of cellular morphology is essential to experimental cell biology. Autoencoders are a popular tool for unsupervised biological image analysis because they learn a low-dimensional representation that maps images to feature vectors to generate a semantically meaningful embedding space of morphological variation. The learned feature vectors can also be used for clustering, dimensionality reduction, outlier detection, and supervised learning problems. Shape properties do not change with orientation, and thus we argue that representation learning methods should encode this orientation invariance. We show that conventional autoencoders are sensitive to orientation, which can lead to suboptimal performance on downstream tasks. To address this, we develop O2-variational autoencoder (O2-VAE), an unsupervised method that learns robust, orientation-invariant representations. We use O2-VAE to discover morphology subgroups in segmented cells and mitochondria, detect outlier cells, and rapidly characterise cellular shape and texture in large datasets, including in a newly generated synthetic benchmark.
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