ArticleScientific reports2022
Automated pancreas segmentation and volumetry using deep neural network on computed tomography.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence in Pancreatic Imaging: A Systematic Review.United European gastroenterology journal · 2025Pooled it
- MKNet-family architectures for auto-segmentation of the residual pancreas after pancreatic resection: a deep learning comparative study.Abdominal radiology (New York) · 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
- Progress in fully automated abdominal CT interpretation-an update over the past decade.Abdominal radiology (New York) · 2026Review
- Deep Learning Model for Volume Measurement of the Remnant Pancreas After Pancreaticoduodenectomy and Distal Pancreatectomy.Diagnostics (Basel, Switzerland) · 2025Article
- Benchmarking robustness of automated CT pancreas segmentation: achieving human-level reliability through human-in-the-loop optimization.Radiology advances · 2025Article
- Unsupervised cross-modality domain adaptation via source-domain labels guided contrastive learning for medical image segmentation.Medical & biological engineering & computing · 2025Article
- AI-Driven insights in pancreatic cancer imaging: from pre-diagnostic detection to prognostication.Abdominal radiology (New York) · 2025Review
- Automated pancreatic extracellular volume fraction as a biomarker for microangiopathy in type 2 diabetes mellitus.BMC medical imaging · 2025Article
- The Laryngovibrogram as a normalized spatiotemporal representation of vocal fold dynamics.Scientific reports · 2025Article
- Pancreatic atrophy after gastric cancer surgery: influencing factors and effects on BMI and quality of life.World journal of surgical oncology · 2025Article
- Artificial intelligence models assisting physicians in quantifying pancreatic necrosis in acute pancreatitis.Quantitative imaging in medicine and surgery · 2025Article
- Large-scale multi-center CT and MRI segmentation of pancreas with deep learning.Medical image analysis · 2025Article
- Advances for Managing Pancreatic Cystic Lesions: Integrating Imaging and AI Innovations.Cancers · 2024Review
- Not Another Dual Attention UNet Transformer (NNDA-UNETR): a plug-and-play parallel dual attention block in U-Net with enhanced residual blocks for medical image segmentation.Quantitative imaging in medicine and surgery · 2024Article
- Pancreatic volume and endocrine function changes following pancreaticoduodenectomy for peri-ampullary neoplasms: A retrospective single-center study utilizing pancreas volumetry.Annals of hepato-biliary-pancreatic surgery · 2024Article
- Pancreas volumes in pediatric patients following index acute pancreatitis and acute recurrent pancreatitis.Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.] · 2024Article
- Article
- Improving Pancreatic Cyst Management: Artificial Intelligence-Powered Prediction of Advanced Neoplasms through Endoscopic Ultrasound-Guided Confocal Endomicroscopy.Biomimetics (Basel, Switzerland) · 2023Review
- Predicting acute pancreatitis severity with enhanced computed tomography scans using convolutional neural networks.Scientific reports · 2023Article
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Authors and funding
6 authors.
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Abstract
Pancreas segmentation is necessary for observing lesions, analyzing anatomical structures, and predicting patient prognosis. Therefore, various studies have designed segmentation models based on convolutional neural networks for pancreas segmentation. However, the deep learning approach is limited by a lack of data, and studies conducted on a large computed tomography dataset are scarce. Therefore, this study aims to perform deep-learning-based semantic segmentation on 1006 participants and evaluate the automatic segmentation performance of the pancreas via four individual three-dimensional segmentation networks. In this study, we performed internal validation with 1,006 patients and external validation using the cancer imaging archive pancreas dataset. We obtained mean precision, recall, and dice similarity coefficients of 0.869, 0.842, and 0.842, respectively, for internal validation via a relevant approach among the four deep learning networks. Using the external dataset, the deep learning network achieved mean precision, recall, and dice similarity coefficients of 0.779, 0.749, and 0.735, respectively. We expect that generalized deep-learning-based systems can assist clinical decisions by providing accurate pancreatic segmentation and quantitative information of the pancreas for abdominal computed tomography.
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