ArticleIEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision2024
AnyStar: Domain randomized universal star-convex 3D instance segmentation.
Article in IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Article
- A quantitative pipeline for whole-mount deep imaging and analysis of multi-layered organoids across scales.eLife · 2026Article
- Synthetic data enables human-grade microtubule analysis with foundation models for segmentation.PLoS computational biology · 2026Article
- Domain-Randomized Deep Learning for Neuroimage Analysis: Selecting Training Strategies, Navigating Challenges, and Maximizing Benefits.IEEE signal processing magazine · 2025Article
- Digitalized organoids: integrated pipeline for high-speed 3D analysis of organoid structures using multilevel segmentation and cellular topology.Nature methods · 2025Article
- Learning General-Purpose Biomedical Volume Representations using Randomized Synthesis.... International Conference on Learning Representations · 2025Article
- Synthetic data in generalizable, learning-based neuroimaging.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Defining the boundaries: challenges and advances in identifying cells in microscopy images.Current opinion in biotechnology · 2024Review
- Shape-aware Segmentation of the Placenta in BOLD Fetal MRI Time Series.The journal of machine learning for biomedical imaging · 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
7 authors.
Funding
Abstract
Star-convex shapes arise across bio-microscopy and radiology in the form of nuclei, nodules, metastases, and other units. Existing instance segmentation networks for such structures train on densely labeled instances for each dataset, which requires substantial and often impractical manual annotation effort. Further, significant reengineering or finetuning is needed when presented with new datasets and imaging modalities due to changes in contrast, shape, orientation, resolution, and density. We present AnyStar, a domain-randomized generative model that simulates synthetic training data of blob-like objects with randomized appearance, environments, and imaging physics to train general-purpose star-convex instance segmentation networks. As a result, networks trained using our generative model do not require annotated images from unseen datasets. A single network trained on our synthesized data accurately 3D segments C. elegans and P. dumerilii nuclei in fluorescence microscopy, mouse cortical nuclei in
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.