ArticleRadiology2025
MRISegmenter: A Fully Accurate and Robust Automated Multiorgan and Structure Segmentation Tool for T1-weighted Abdominal MRI.
Article in Radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Utility of Pancreatic Perivascular Adipose Tissue as a CT Imaging Biomarker for Diagnosing Type 2 Diabetes.Diabetes · 2026Article
- Fully automated diaphragm segmentation on routine 2D-cine and 3D thoracic MRI: multireader validation.European radiology experimental · 2026Article
- Automatic probe guidance for volumetric liver ultrasound acquisition via imitation learning from a virtual expert.International journal of computer assisted radiology and surgery · 2026Article
- Assessment of Robustness of MRI Radiomic Features in Four Abdominal Organs: Impact of Deep Learning Reconstruction and Segmentation.Journal of magnetic resonance imaging : JMRI · 2026Article
- Metadata Conditioning via Feature-Wise Linear Modulation for Multi-Domain 3D Abdominal Segmentation: A Comparative Study Between U-Net and nnU-Net.Sensors (Basel, Switzerland) · 2026Article
- Artificial Intelligence-Based Approach for Automated Gonad Volume Quantification Using Magnetic Resonance Imaging in Healthy Adolescents Across Puberty.Diagnostics (Basel, Switzerland) · 2026Article
- VIBESegmentator: full body MRI segmentation for the NAKO and UK Biobank.European radiology · 2026Article
- Spatial characterization of intrahepatic fat deposition across steatotic liver disease subcategories: a multicenter study.BMC medicine · 2026Article
- Frenet-Serret Frame-Based Decomposition for Part Segmentation of 3-D Curvilinear Structures.IEEE transactions on medical imaging · 2025Article
- MRI annotation using an inversion-based preprocessing for CT model adaptation.European radiology experimental · 2025Article
- Automated Classification of Body MRI Sequences Using Convolutional Neural Networks.Academic radiology · 2025Article
- Weakly supervised detection of pheochromocytomas and paragangliomas in CT using noisy data.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2024Article
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Abstract
Background There is a pressing demand to develop an automated segmentation tool for abdominal MRI that can provide accurate and robust segmentation in more than 60 abdominal organs and structures. Purpose To develop and evaluate the accuracy and robustness of an automated multiorgan and structure segmentation tool for T1-weighted abdominal MRI. Materials and Methods In this retrospective study, a T1-weighted abdominal MRI dataset composed of axial precontrast T1-weighted and contrast-enhanced T1-weighted arterial, portal venous, and delayed phases for each patient in a randomly selected sample was included at the National Institutes of Health Clinical Center. Each MRI series contained voxel-level annotations of 62 abdominal organs and structures. A three-dimensional segmentation (nnU-Net) model, called MRISegmenter, was trained on this dataset. This internal dataset was then randomly split into training and internal test sets. Evaluation was conducted on the internal test set and two external test sets (Abdominal Multi-Organ Segmentation Challenge 2022 [AMOS22] and Duke Liver). The predicted segmentations were compared against the radiologist-verified reference standard annotations using means ± SDs for the Dice similarity coefficient (Dice score) and normalized surface distance (NSD). The segmentation tool and dataset are publicly available at
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