ArticleeLife2023
MorphoFeatures for unsupervised exploration of cell types, tissues, and organs in volume electron microscopy.
Article in eLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 25 citations in OpenAlex.
- Reclaiming Anatomy as Method: From Morphological Reasoning to Clinical Relevance.Clinical anatomy (New York, N.Y.) · 2026Review
- Analysis of multicellular anatomical structures from spatial omics data using sosta.PLoS computational biology · 2026Article
- Artificial intelligence-powered microscopy: Transforming the landscape of parasitology.Journal of microscopy · 2026Review
- Cellular Hallmarks From Volume Electron Microscopy Reveal Developmental Progression of Plasmodium Ookinetes.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Quantifying the Single-Cell Morphological Landscape of Cellular Transdifferentiation through Force Field Reconstruction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- From cells to pixels: A decision tree for designing bioimage analysis pipelines.Journal of microscopy · 2025Article
- A deep learning approach for time-consistent cell cycle phase prediction from microscopy data.PLoS computational biology · 2025Article
- Review
- A self-supervised learning approach for high throughput and high content cell segmentation.Communications biology · 2025Article
- Article
- Article
- Novel imaging and biophysical approaches to study tissue hydraulics in mammalian folliculogenesis.Biophysical reviews · 2024Review
- Multi-layered maps of neuropil with segmentation-guided contrastive learning.Nature methods · 2023Article
- Finding the right type of cell.eLife · 2023Article
- Virtual Fly Brain-An interactive atlas of theFrontiers in physiology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Electron microscopy (EM) provides a uniquely detailed view of cellular morphology, including organelles and fine subcellular ultrastructure. While the acquisition and (semi-)automatic segmentation of multicellular EM volumes are now becoming routine, large-scale analysis remains severely limited by the lack of generally applicable pipelines for automatic extraction of comprehensive morphological descriptors. Here, we present a novel unsupervised method for learning cellular morphology features directly from 3D EM data: a neural network delivers a representation of cells by shape and ultrastructure. Applied to the full volume of an entire three-segmented worm of the annelid
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.