ArticleNature communications2020
Deep learning-enabled multi-organ segmentation in whole-body mouse scans.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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Who cites it
52 citing papers in PubMed.
- AI-Based Multi-Organ Segmentation in Gynecologic Laparoscopy: Comparative Evaluation of Deep Learning Architectures for Anatomical Precision and Surgical Applicability.Journal of imaging informatics in medicine · 2026Article
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- High Throughput In vivo Imaging; A Pipeline for Automated Analysis of Longitudinal Preclinical Studies.Current protocols · 2026Article
- Progress in fully automated abdominal CT interpretation-an update over the past decade.Abdominal radiology (New York) · 2026Review
- A pipeline for developing deep learning prognostic prediction models in cardiac magnetic resonance image analysis.European heart journal. Digital health · 2026Article
- Deep learning-based detection of murine congenital heart defects from µCT scans.Communications biology · 2025Article
- Laser-Ablated Gold Nanoparticles as Tunable Contrast Agents for Preclinical Imaging.Nanomaterials (Basel, Switzerland) · 2025Article
- Article
- 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
- Advanced quantification pipeline reveals new spatial and temporal tumor characteristics in preclinical multiple myeloma.EJNMMI research · 2025Article
- Ensemble methods and partially-supervised learning for accurate and robust automatic murine organ segmentation.Scientific reports · 2025Article
- Imaging technologies in experimental pulmonary fibrosis research: essential tool for enhanced translational relevance.European respiratory review : an official journal of the European Respiratory Society · 2025Review
- Unveiling Another Dimension: Advanced Visualization of Cancer Invasion and Metastasis via Micro-CT Imaging.Cancers · 2025Article
- A robust deep learning approach for segmenting cortical and trabecular bone from 3D high resolution µCT scans of mouse bone.Scientific reports · 2025Article
- Article
- Development of anatomically accurate digital organ models for surgical simulation and training.PloS one · 2025Article
- Robust Automated Mouse Micro-CT Segmentation Using Swin UNEt TRansformers.Bioengineering (Basel, Switzerland) · 2024Article
- Development of a PTV margin for preclinical irradiation of orthotopic pancreatic tumors derived from a well-known recipe for humans.Zeitschrift fur medizinische Physik · 2024Article
- Multiscale and multimodal evaluation of autosomal dominant polycystic kidney disease development.Communications biology · 2024Article
- Applications of Lightsheet Fluorescence Microscopy by High Numerical Aperture Detection Lens.The journal of physical chemistry. B · 2024Review
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Whole-body imaging of mice is a key source of information for research. Organ segmentation is a prerequisite for quantitative analysis but is a tedious and error-prone task if done manually. Here, we present a deep learning solution called AIMOS that automatically segments major organs (brain, lungs, heart, liver, kidneys, spleen, bladder, stomach, intestine) and the skeleton in less than a second, orders of magnitude faster than prior algorithms. AIMOS matches or exceeds the segmentation quality of state-of-the-art approaches and of human experts. We exemplify direct applicability for biomedical research for localizing cancer metastases. Furthermore, we show that expert annotations are subject to human error and bias. As a consequence, we show that at least two independently created annotations are needed to assess model performance. Importantly, AIMOS addresses the issue of human bias by identifying the regions where humans are most likely to disagree, and thereby localizes and quantifies this uncertainty for improved downstream analysis. In summary, AIMOS is a powerful open-source tool to increase scalability, reduce bias, and foster reproducibility in many areas of biomedical research.
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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.