ArticleMedical image analysis2025
Large-scale multi-center CT and MRI segmentation of pancreas with deep learning.
Article in Medical image analysis, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence-based models for quantification of intra-pancreatic fat deposition and their clinical relevance: a systematic review of imaging studies.European radiology · 2026Pooled it
- Artificial intelligence in pancreatic cancer: applications in early detection, tumor staging, and survival prediction-a comprehensive review.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Multicenter Validation of Foundation Model Adaptation for Automated Pancreatic Tumor Delineation on CT Scans.Cancers · 2026Article
- MRICombo: a deep-learning-based framework for universal volumetric segmentation grading-staging and malignancy detection across heterogeneous MRI.Nature communications · 2026Article
- Multi-center evaluation of radiomics and deep learning to stratify malignancy risk of IPMNs.Abdominal radiology (New York) · 2026Article
- Large-scale multi-sequence pretraining for generalizable MRI analysis in versatile clinical applications.Nature biomedical engineering · 2026Article
- A Reproducible Multicentre MRI Radiomics Workflow for Pancreatic Cyst Risk Stratification Using Paired T1- and T2-Weighted Imaging.Tomography (Ann Arbor, Mich.) · 2026Article
- Multimodal AI for early prediction of adverse clinical outcomes in acute pancreatitis.Abdominal radiology (New York) · 2026Article
- Clinically interpretable nomogram incorporating radiomics and deep learning feature fusion from abdominal CT for preclinical type 2 diabetes.Abdominal radiology (New York) · 2026Article
- Pancreatic tumor detection in computed tomography images through a rotary positional siamese vision transformer.Scientific reports · 2026Article
- A Vision Transformer-Based Deep Learning Framework for Patient-Level Classification of Acute Pancreatitis and Normal Pancreas Using Computed Tomography.Diagnostics (Basel, Switzerland) · 2026Article
- Deep learning based on ultrasound for differential diagnosis of pancreatic serous cystic neoplasm and mucinous cystic neoplasm.BMC cancer · 2025Article
- Benchmarking robustness of automated CT pancreas segmentation: achieving human-level reliability through human-in-the-loop optimization.Radiology advances · 2025Article
- Deep learning automatic segmentation and radiomics model for diagnosing pancreatic solid neoplasms in MRI.BMC cancer · 2025Article
- Comparison of publicly available artificial intelligence models for pancreatic segmentation on T1-weighted Dixon images.Japanese journal of radiology · 2025Article
- Automated Quantitative Evaluation of Age-Related Thymic Involution on Plain Chest CT.Annals of biomedical engineering · 2025Article
- A dual self-attentive transformer U-Net model for precise pancreatic segmentation and fat fraction estimation.BMC medical imaging · 2025Article
- Pediatric pancreas segmentation from MRI scans with deep learning.Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.] · 2025Article
- Decentralized Personalization for Federated Medical Image Segmentation via Gossip Contrastive Mutual Learning.IEEE transactions on medical imaging · 2025Article
- AI-Driven insights in pancreatic cancer imaging: from pre-diagnostic detection to prognostication.Abdominal radiology (New York) · 2025Review
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Abstract
Automated volumetric segmentation of the pancreas on cross-sectional imaging is needed for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic segmentation is more established, MRI-based segmentation methods are understudied, largely due to a lack of publicly available datasets, benchmarking research efforts, and domain-specific deep learning methods. In this retrospective study, we collected a large dataset (767 scans from 499 participants) of T1-weighted (T1 W) and T2-weighted (T2 W) abdominal MRI series from five centers between March 2004 and November 2022. We also collected CT scans of 1,350 patients from publicly available sources for benchmarking purposes. We introduced a new pancreas segmentation method, called PanSegNet, combining the strengths of nnUNet and a Transformer network with a new linear attention module enabling volumetric computation. We tested PanSegNet's accuracy in cross-modality (a total of 2,117 scans) and cross-center settings with Dice and Hausdorff distance (HD95) evaluation metrics. We used Cohen's kappa statistics for intra and inter-rater agreement evaluation and paired t-tests for volume and Dice comparisons, respectively. For segmentation accuracy, we achieved Dice coefficients of 88.3% (±7.2%, at case level) with CT, 85.0% (±7.9%) with T1 W MRI, and 86.3% (±6.4%) with T2 W MRI. There was a high correlation for pancreas volume prediction with R
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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 Socratic graph.