ArticleBiology open2023
Deep learning enabled multi-organ segmentation of mouse embryos.
Article in Biology open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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.
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Who cites it
12 citing papers in PubMed.
- MorphoCloud: Democratizing Access to High-Performance Computing for Morphological Data Analysis.F1000Research · 2026Article
- Anatomy-aware, label-informed approach improves image registration for challenging datasets.PloS one · 2026Article
- MorphoCloud: Democratizing Access to High-Performance Computing for Morphological Data Analysis.ArXiv · 2025Article
- The ANTsX ecosystem for mapping the mouse brain.Nature communications · 2025Article
- Streamlining asymmetry quantification in fetal mouse imaging: A semi-automated pipeline supported by expert guidance.Developmental dynamics : an official publication of the American Association of Anatomists · 2025Article
- The SmARTR pipeline: A modular workflow for the cinematic rendering of 3D scientific imaging data.iScience · 2024Article
- Robust Automated Mouse Micro-CT Segmentation Using Swin UNEt TRansformers.Bioengineering (Basel, Switzerland) · 2024Article
- Application of diceCT to Study the Development of the Zika Virus-Infected Mouse Brain.Viruses · 2024Article
- Article
- ChFluids and barriers of the CNS · 2024Article
- Opportunities and Challenges in Applying AI to Evolutionary Morphology.Integrative organismal biology (Oxford, England) · 2024Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The International Mouse Phenotyping Consortium (IMPC) has generated a large repository of three-dimensional (3D) imaging data from mouse embryos, providing a rich resource for investigating phenotype/genotype interactions. While the data is freely available, the computing resources and human effort required to segment these images for analysis of individual structures can create a significant hurdle for research. In this paper, we present an open source, deep learning-enabled tool, Mouse Embryo Multi-Organ Segmentation (MEMOS), that estimates a segmentation of 50 anatomical structures with a support for manually reviewing, editing, and analyzing the estimated segmentation in a single application. MEMOS is implemented as an extension on the 3D Slicer platform and is designed to be accessible to researchers without coding experience. We validate the performance of MEMOS-generated segmentations through comparison to state-of-the-art atlas-based segmentation and quantification of previously reported anatomical abnormalities in a Cbx4 knockout strain. This article has an associated First Person interview with the first author of the paper.
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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.