ArticleMedical physics2020
CT-based multi-organ segmentation using a 3D self-attention U-net network for pancreatic radiotherapy.
Article in Medical physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence in Pancreatic Imaging: A Systematic Review.United European gastroenterology journal · 2025Pooled it
- Deep learning-based artificial intelligence can improve the diagnosis of small bowel obstruction: stratified comparison study and hierarchical Bayesian model.Scientific reports · 2026Article
- A few-shot u-net learning framework for fast and accurate three-dimensional dose prediction in radiotherapy.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2025Article
- Federated prediction for scalable and privacy-preserved knowledge-based planning in radiotherapy.ArXiv · 2025Article
- Sex-Based Bias in Artificial Intelligence-Based Segmentation Models in Clinical Oncology.Clinical oncology (Royal College of Radiologists (Great Britain)) · 2025Review
- Automated abdominal organ segmentation algorithms for non-enhanced CT for volumetry and 3D radiomics analysis.Abdominal radiology (New York) · 2025Article
- 2D medical image synthesis using transformer-based denoising diffusion probabilistic model.Physics in medicine and biology · 2023Article
- Abdomen CT multi-organ segmentation using token-based MLP-Mixer.Medical physics · 2023Article
- Differentiation of malignant from benign pleural effusions based on artificial intelligence.Thorax · 2023Article
- CT-free quantitative SPECT for automatic evaluation of %thyroid uptake based on deep-learning.EJNMMI physics · 2023Article
- Automated lung tumor delineation on positron emission tomography/computed tomography via a hybrid regional network.Medical physics · 2023Article
- Fast myocardial perfusion SPECT denoising using an attention-guided generative adversarial network.Frontiers in medicine · 2023Article
- Multi-organ segmentation of abdominal structures from non-contrast and contrast enhanced CT images.Scientific reports · 2022Article
- Nested block self-attention multiple resolution residual network for multiorgan segmentation from CT.Medical physics · 2022Article
- Impact of Denoising on Deep-Learning-Based Automatic Segmentation Framework for Breast Cancer Radiotherapy Planning.Cancers · 2022Article
- A Survey on Deep Learning for Precision Oncology.Diagnostics (Basel, Switzerland) · 2022Review
- Application of artificial intelligence to pancreatic adenocarcinoma.Frontiers in oncology · 2022Review
- Prostate and dominant intraprostatic lesion segmentation on PET/CT using cascaded regional-net.Physics in medicine and biology · 2021Article
- Image intensity histograms as imaging biomarkers: application to immune-related colitis.Biomedical physics & engineering express · 2021Article
- Hyperpolarized Magnetic Resonance and Artificial Intelligence: Frontiers of Imaging in Pancreatic Cancer.JMIR medical informatics · 2021Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
purposeSegmentation of organs-at-risk (OARs) is a weak link in radiotherapeutic treatment planning process because the manual contouring action is labor-intensive and time-consuming. This work aimed to develop a deep learning-based method for rapid and accurate pancreatic multi-organ segmentation that can expedite the treatment planning process.
methodsWe retrospectively investigated one hundred patients with computed tomography (CT) simulation scanned and contours delineated. Eight OARs including large bowel, small bowel, duodenum, left kidney, right kidney, liver, spinal cord and stomach were the target organs to be segmented. The proposed three-dimensional (3D) deep attention U-Net is featured with a deep attention strategy to effectively differentiate multiple organs. Performance of the proposed method was evaluated using six metrics, including Dice similarity coefficient (DSC), sensitivity, specificity, Hausdorff distance 95% (HD95), mean surface distance (MSD) and residual mean square distance (RMSD).
resultsThe contours generated by the proposed method closely resemble the ground-truth manual contours, as evidenced by encouraging quantitative results in terms of DSC, sensitivity, specificity, HD95, MSD and RMSD. For DSC, mean values of 0.91 ± 0.03, 0.89 ± 0.06, 0.86 ± 0.06, 0.95 ± 0.02, 0.95 ± 0.02, 0.96 ± 0.01, 0.87 ± 0.05 and 0.93 ± 0.03 were achieved for large bowel, small bowel, duodenum, left kidney, right kidney, liver, spinal cord and stomach, respectively.
conclusionsThe proposed method could significantly expedite the treatment planning process by rapidly segmenting multiple OARs. The method could potentially be used in pancreatic adaptive radiotherapy to increase dose delivery accuracy and minimize gastrointestinal toxicity.
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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 Socratic graph.