ArticlePhysics in medicine and biology2017
Combining deep learning with anatomical analysis for segmentation of the portal vein for liver SBRT planning.
Article in Physics in medicine and biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04299919 (The Artificial Intelligent Prognostic Assessment and Pathological Basis Research of Early Primary Hepatocellular Carcinoma After Minimally Invasive Treatment Based on Multimodal MRI and Clinical Big Data), which is not on this map. Cited by 35 papers.
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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.
The Artificial Intelligent Prognostic Assessment and Pathological Basis Research of Early Primary Hepatocellular Carcinoma After Minimally Invasive Treatment Based on Multimodal MRI and Clinical Big Data
Who cites it
35 citing papers in PubMed, 88 citations in OpenAlex.
- A study of cross-dataset generalization on liver vessel segmentation improved by a topological loss.Medical & biological engineering & computing · 2026Article
- Comparison of Manual, Semi-Automatic, and Automatic CT-Based Methods for Liver Volume Segmentation.Diagnostics (Basel, Switzerland) · 2026Article
- Improved segmentation of hepatic vascular networks in ultrasound volumes using 3D U-Net with intensity transformation-based data augmentation.Medical & biological engineering & computing · 2025Article
- Fully Automatic Artificial Intelligence Liver Anatomy Segmentation in the Management of Colorectal Liver Metastases.Cureus · 2025Article
- Centerline-guided reinforcement learning model for pancreatic duct identifications.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Vascular liver segmentation: a narrative review on methods and new insights brought by artificial intelligence.The Journal of international medical research · 2024Review
- Compensation of small data with large filters for accurate liver vessel segmentation from contrast-enhanced CT images.BMC medical imaging · 2024Article
- Suitability of DNN-based vessel segmentation for SIRT planning.International journal of computer assisted radiology and surgery · 2024Article
- Deep learning for automated segmentation in radiotherapy: a narrative review.The British journal of radiology · 2024Review
- Machine learning-based identification of contrast-enhancement phase of computed tomography scans.PloS one · 2024Article
- Artificial intelligence techniques in liver cancer.Frontiers in oncology · 2024Review
- An overview of artificial intelligence in medical physics and radiation oncology.Journal of the National Cancer Center · 2023Review
- Segmentation of Portal Vein in Multiphase CTA Image Based on Unsupervised Domain Transfer and Pseudo Label.Diagnostics (Basel, Switzerland) · 2023Article
- Hepatic vessels segmentation using deep learning and preprocessing enhancement.Journal of applied clinical medical physics · 2023Article
- Techniques and Algorithms for Hepatic Vessel Skeletonization in Medical Images: A Survey.Entropy (Basel, Switzerland) · 2022Review
- Hepatic Vein and Arterial Vessel Segmentation in Liver Tumor Patients.Computational intelligence and neuroscience · 2022Article
- Artificial intelligence in image-guided radiotherapy: a review of treatment target localization.Quantitative imaging in medicine and surgery · 2021Review
- What is new in computer vision and artificial intelligence in medical image analysis applications.Quantitative imaging in medicine and surgery · 2021Review
- Artificial intelligence in assessment of hepatocellular carcinoma treatment response.Abdominal radiology (New York) · 2021Review
- The overview of the deep learning integrated into the medical imaging of liver: a review.Hepatology international · 2021Review
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Automated segmentation of the portal vein (PV) for liver radiotherapy planning is a challenging task due to potentially low vasculature contrast, complex PV anatomy and image artifacts originated from fiducial markers and vasculature stents. In this paper, we propose a novel framework for automated segmentation of the PV from computed tomography (CT) images. We apply convolutional neural networks (CNNs) to learn the consistent appearance patterns of the PV using a training set of CT images with reference annotations and then enhance the PV in previously unseen CT images. Markov random fields (MRFs) were further used to smooth the results of the enhancement of the CNN enhancement and remove isolated mis-segmented regions. Finally, CNN-MRF-based enhancement was augmented with PV centerline detection that relied on PV anatomical properties such as tubularity and branch composition. The framework was validated on a clinical database with 72 CT images of patients scheduled for liver stereotactic body radiation therapy. The obtained accuracy of the segmentation was [Formula: see text] 0.83 and [Formula: see text] 1.08 mm in terms of the median Dice coefficient and mean symmetric surface distance, respectively, when segmentation is encompassed into the PV region of interest. The obtained results indicate that CNNs and anatomical analysis can be used for the accurate segmentation of the PV and potentially integrated into liver radiation therapy planning.
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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 OpenQuestion graph.