ArticleeLife2020
Accurate and versatile 3D segmentation of plant tissues at cellular resolution.
Article in eLife, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 136 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.
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Who cites it
136 citing papers in PubMed.
- Reviewing reproducibility and replicability in 'Deconstructing gastrulation at single-cell resolution' by Stern et al. (2022).Journal of microscopy · 2026Review
- Unlocking the full potential of spatial omics in plants: practical challenges, solutions, and a path forward.The Plant cell · 2026Review
- Cell-based crop phenotyping for future climates.The New phytologist · 2026Review
- Article
- Single-Cell Insights into Medicinal Plant Development and Metabolism.Plants (Basel, Switzerland) · 2026Review
- SpotMAX: A generalist framework for multidimensional automatic spot detection and quantification.Science advances · 2026Article
- A Visual Question Answering Dataset for Benchmarking Vision-Language Models in Plant Science.Scientific data · 2026Article
- Single-Cell and Spatial Omics Technologies in Rice Abiotic Stress Biology: A Methodological Review.International journal of molecular sciences · 2026Review
- Spotlight on challenges and novel methods in highly multiplexed tissue imaging-based spatial proteomics.Journal of translational medicine · 2026Review
- Stomatalia: a deep learning-based platform for quantitative stomata and pavement cell analysis.Plant methods · 2026Article
- A deep learning-enabled toolkit for the 3D segmentation of ventricular cardiomyocytes.The Journal of physiology · 2026Article
- SKOOTS: Skeleton-Oriented Object Segmentation for Mitochondria in High-Resolution Cochlear EM Datasets.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- DEEP-DISORDER: Motion Correction in 3D MRI via Segment Reconstruction and Registration.NMR in biomedicine · 2026Article
- Auto-segmentation of organs-of-interest clinical acceptability & reproducibility framework in head and neck cancer.Physics and imaging in radiation oncology · 2026Article
- Synthetic data enables human-grade microtubule analysis with foundation models for segmentation.PLoS computational biology · 2026Article
- Persistent homology-based segmentation tool for membrane images.Cell reports methods · 2026Article
- ICECREAM: high-fidelity equivariant cryo-electron tomography.Acta crystallographica. Section D, Structural biology · 2026Article
- EpiCure (Epithelial Curation): a versatile and handy tool for curation of epithelial segmentation.bioRxiv : the preprint server for biology · 2026Article
- MBMSA-UNet: A Multi-Scale Attention-Based Instance Segmentation Model for Moso Bamboo Cells.Plants (Basel, Switzerland) · 2026Article
- Spiral phyllotaxis in the moss Physcomitrium patens emerges from simple division rules of the apical cell.Current biology : CB · 2026Article
76 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
21 authors.
Funding
Abstract
Quantitative analysis of plant and animal morphogenesis requires accurate segmentation of individual cells in volumetric images of growing organs. In the last years, deep learning has provided robust automated algorithms that approach human performance, with applications to bio-image analysis now starting to emerge. Here, we present PlantSeg, a pipeline for volumetric segmentation of plant tissues into cells. PlantSeg employs a convolutional neural network to predict cell boundaries and graph partitioning to segment cells based on the neural network predictions. PlantSeg was trained on fixed and live plant organs imaged with confocal and light sheet microscopes. PlantSeg delivers accurate results and generalizes well across different tissues, scales, acquisition settings even on non plant samples. We present results of PlantSeg applications in diverse developmental contexts. PlantSeg is free and open-source, with both a command line and a user-friendly graphical interface.
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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.