ArticleJournal of imaging informatics in medicine2025
Development and Validation of a Modality-Invariant 3D Swin U-Net Transformer for Liver and Spleen Segmentation on Multi-Site Clinical Bi-parametric MR Images.
Article in Journal of imaging informatics in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- The role of artificial intelligence in paediatric abdominal imaging.Pediatric radiology · 2026Review
- Development and validation of a deep learning model for liver shear stiffness regression using abdominal multiparametric MRI across multiple sites and vendors.European radiology · 2026Article
- Generalized U-Net for automatic liver segmentation and R2* estimation for assessment of iron overload using MRI.BMC medical imaging · 2026Article
- Benchmark-Driven Clinical Decision Framework for Multi-Class Middle Ear Disease Diagnosis: Superiority of Swin Transformer in Accuracy and Stability.Diagnostics (Basel, Switzerland) · 2026Article
- Use of Patient-Specific 3D Models in Paediatric Surgery: Effect on Communication and Surgical Management.Journal of imaging · 2026Article
- Deformable dose prediction network based on hybrid 2D and 3D convolution for nasopharyngeal carcinoma radiotherapy.Medical & biological engineering & computing · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
To develop and validate a modality-invariant Swin U-Net Transformer (UNETR) deep learning model for liver and spleen segmentation on abdominal T1-weighted (T1w) or T2-weighted (T2w) MR images from multiple institutions for pediatric and adult patients with known or suspected chronic liver diseases. In this IRB-approved retrospective study, clinical abdominal axial T1w and T2w MR images from pediatric and adult patients were retrieved from four study sites, including Cincinnati Children's Hospital Medical Center (CCHMC), New York University (NYU), University of Wisconsin (UW) and University of Michigan / Michigan Medicine (UM). The whole liver and spleen were manually delineated as the ground truth masks. We developed a modality-invariant 3D Swin UNETR using a modality-invariant training strategy, in which each patient's T1w and T2w MR images were treated as separate training samples. We conducted both internal and external validation experiments. A total of 241 T1w and 339 T2w MR sequences from 304 patients (age [mean
Indexed as
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.