ArticleMolecular biology of the cell2023
Evaluation of cell segmentation methods without reference segmentations.
Article in Molecular biology of the cell, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
25 citing papers in PubMed.
- Transforming subcellular spatial transcriptomics: deep learning models for cell segmentation.Nucleic acids research · 2026Review
- Extracellular matrix phenotyping by imaging mass cytometry defines distinct cellular matrix environments associated with allergic airway inflammation.Molecular systems biology · 2026Article
- A deterministic method for quantifying spindle-shaped cells in noisy bright-field microscopy.Scientific reports · 2026Article
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- SenNet Portal: Build, Optimization and Usage.bioRxiv : the preprint server for biology · 2026Article
- SPRM: spatial process and relationship modeling for multiplexed images.Bioinformatics advances · 2026Article
- A novel single-cell level evaluation method for corneal endothelial cell function.NPJ digital medicine · 2025Article
- From cells to pixels: A decision tree for designing bioimage analysis pipelines.Journal of microscopy · 2025Article
- Advancing in vitro cell migration studies: a review of open-source analytical platforms for cancer and wound healing research.Cell adhesion & migration · 2025Review
- Toggle-Untoggle - a cell segmentation tool with an interactive user verification interface.Journal of cell science · 2025Article
- Investigation of quantitative structural changes in astrocyte cells after laser-induced shockwave.Biophotonics discovery · 2025Article
- 3DCellComposer - A versatile pipeline utilizing 2D cell segmentation methods for 3D cell segmentation.Methods (San Diego, Calif.) · 2025Article
- Rethinking deep learning in bioimaging through a data centric lens.Npj imaging · 2025Article
- Attention-driven UNet enhancement for accurate segmentation of bacterial spore outgrowth in microscopy images.Scientific reports · 2025Article
- Quantitative benchmarking of nuclear segmentation algorithms in multiplexed immunofluorescence imaging for translational studies.Communications biology · 2025Article
- Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas construction and usage.Nature methods · 2025Article
- Automated prediction of fibroblast phenotypes using mathematical descriptors of cellular features.Nature communications · 2025Article
- Robust consensus nuclear and cell segmentation.Frontiers in genetics · 2025Article
- A generative benchmark for evaluating the performance of fluorescent cell image segmentation.Synthetic and systems biotechnology · 2024Article
- Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas Construction and Usage.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Cell segmentation is a cornerstone of many bioimage informatics studies, and inaccurate segmentation introduces error in downstream analysis. Evaluating segmentation results is thus a necessary step for developing segmentation methods as well as for choosing the most appropriate method for a particular type of sample. The evaluation process has typically involved comparison of segmentations with those generated by humans, which can be expensive and subject to unknown bias. We present here an approach to evaluating cell segmentation methods without relying upon comparison to results from humans. For this, we defined a number of segmentation quality metrics that can be applied to multichannel fluorescence images. We calculated these metrics for 14 previously described segmentation methods applied to datasets from four multiplexed microscope modalities covering five tissues. Using principal component analysis to combine the metrics, we defined an overall cell segmentation quality score and ranked the segmentation methods. We found that two deep learning-based methods performed the best overall, but that results for all methods could be significantly improved by postprocessing to ensure proper matching of cell and nuclear masks. Our evaluation tool is available as open source and all code and data are available in a Reproducible Research Archive.
Identifiers
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.