ArticleScientific reports2022
Segmentation of pancreatic ductal adenocarcinoma (PDAC) and surrounding vessels in CT images using deep convolutional neural networks and texture descriptors.
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 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence in Pancreatic Imaging: A Systematic Review.United European gastroenterology journal · 2025Pooled it
- Bounding box-based 3D AI model for user-guided volumetric segmentation of pancreatic ductal adenocarcinoma on standard-of-care CTs.Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.] · 2023Trial
- Multicenter Validation of Foundation Model Adaptation for Automated Pancreatic Tumor Delineation on CT Scans.Cancers · 2026Article
- Refining the Classroom: The Self-Supervised Professor Model for Improved Segmentation of Locally Advanced Pancreatic Ductal Adenocarcinoma.Journal of imaging informatics in medicine · 2026Article
- Pancreatic tumor detection in computed tomography images through a rotary positional siamese vision transformer.Scientific reports · 2026Article
- Pancreatic multi structure segmentation on CT using transformer and deep learning approaches.Discover oncology · 2026Article
- Multi-class transformer-based segmentation of pancreatic ductal adenocarcinoma and surrounding structures in CT imaging: a multi-center evaluation.Abdominal radiology (New York) · 2026Article
- Application of Artificial Intelligence in Pancreatic Cyst Management: A Systematic Review.Cancers · 2025Review
- AI-Driven insights in pancreatic cancer imaging: from pre-diagnostic detection to prognostication.Abdominal radiology (New York) · 2025Review
- Toward diffusion MRI in the diagnosis and treatment of pancreatic cancer.Medical oncology (Northwood, London, England) · 2025Review
- An optimized two stage U-Net approach for segmentation of pancreas and pancreatic tumor.MethodsX · 2024Article
- Anatomical attention can help to segment the dilated pancreatic duct in abdominal CT.International journal of computer assisted radiology and surgery · 2024Article
- Imaging-based deep learning in kidney diseases: recent progress and future prospects.Insights into imaging · 2024Review
- Pancreatic Adenocarcinoma: Imaging Modalities and the Role of Artificial Intelligence in Analyzing CT and MRI Images.Diagnostics (Basel, Switzerland) · 2024Review
- From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer.Diagnostics (Basel, Switzerland) · 2024Review
- Combined analytical approach empowers precise spectroscopic interpretation of subcellular components of pancreatic cancer cells.Analytical and bioanalytical chemistry · 2023Article
- Identifying Effective Biomarkers for Accurate Pancreatic Cancer Prognosis Using Statistical Machine Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Artificial Intelligence in Endoscopic Ultrasonography-Guided Fine-Needle Aspiration/Biopsy (EUS-FNA/B) for Solid Pancreatic Lesions: Opportunities and Challenges.Diagnostics (Basel, Switzerland) · 2023Review
- Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review.Journal of medical Internet research · 2023Article
- Artificial intelligence as a noninvasive tool for pancreatic cancer prediction and diagnosis.World journal of gastroenterology · 2023Review
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8 authors.
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Abstract
Fully automated and volumetric segmentation of critical tumors may play a crucial role in diagnosis and surgical planning. One of the most challenging tumor segmentation tasks is localization of pancreatic ductal adenocarcinoma (PDAC). Exclusive application of conventional methods does not appear promising. Deep learning approaches has achieved great success in the computer aided diagnosis, especially in biomedical image segmentation. This paper introduces a framework based on convolutional neural network (CNN) for segmentation of PDAC mass and surrounding vessels in CT images by incorporating powerful classic features, as well. First, a 3D-CNN architecture is used to localize the pancreas region from the whole CT volume using 3D Local Binary Pattern (LBP) map of the original image. Segmentation of PDAC mass is subsequently performed using 2D attention U-Net and Texture Attention U-Net (TAU-Net). TAU-Net is introduced by fusion of dense Scale-Invariant Feature Transform (SIFT) and LBP descriptors into the attention U-Net. An ensemble model is then used to cumulate the advantages of both networks using a 3D-CNN. In addition, to reduce the effects of imbalanced data, a multi-objective loss function is proposed as a weighted combination of three classic losses including Generalized Dice Loss (GDL), Weighted Pixel-Wise Cross Entropy loss (WPCE) and boundary loss. Due to insufficient sample size for vessel segmentation, we used the above-mentioned pre-trained networks and fine-tuned them. Experimental results show that the proposed method improves the Dice score for PDAC mass segmentation in portal-venous phase by 7.52% compared to state-of-the-art methods in term of DSC. Besides, three dimensional visualization of the tumor and surrounding vessels can facilitate the evaluation of PDAC treatment response.
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