ArticleBioMed research international2019
Fully Automated Segmentation of Lower Extremity Deep Vein Thrombosis Using Convolutional Neural Network.
Article in BioMed research international, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 27 citations in OpenAlex.
- Development and validation of the IVENUS artificial intelligence model for automated CEAP classification of early-stage chronic venous disease using lower limb photographs.Journal of vascular surgery. Venous and lymphatic disorders · 2026Article
- Privacy-aware deep vein thrombosis segmentation using a multi-model federated learning framework with the federated averaging algorithm.Scientific reports · 2026Article
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- Unveiling the potential of artificial intelligence in revolutionizing disease diagnosis and prediction: a comprehensive review of machine learning and deep learning approaches.European journal of medical research · 2025Review
- Innovative modified-net architecture: enhanced segmentation of deep vein thrombosis.Scientific reports · 2024Article
- Numerical simulation and in vitro experimental study of the hemodynamic performance of vena cava filters with helical forms.Scientific reports · 2024Article
- Machine learning in cancer-associated thrombosis: hype or hope in untangling the clot.Bleeding, thrombosis and vascular biology · 2024Article
- Automated Segmentation of Intracranial Thrombus on NCCT and CTA in Patients with Acute Ischemic Stroke Using a Coarse-to-Fine Deep Learning Model.AJNR. American journal of neuroradiology · 2023Article
- Artificial intelligence-based iliofemoral deep venous thrombosis detection using a clinical approach.Scientific reports · 2023Article
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- Application of artificial intelligence for automatic cataract staging based on anterior segment images: comparing automatic segmentation approaches to manual segmentation.Frontiers in neuroscience · 2023Article
- Comparisons of artificial intelligence algorithms in automatic segmentation for fungal keratitis diagnosis by anterior segment images.Frontiers in neuroscience · 2023Article
- Artificial Intelligence Evidence-Based Current Status and Potential for Lower Limb Vascular Management.Journal of personalized medicine · 2021Review
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveDeep vein thrombosis (DVT) is a disease caused by abnormal blood clots in deep veins. Accurate segmentation of DVT is important to facilitate the diagnosis and treatment. In the current study, we proposed a fully automatic method of DVT delineation based on deep learning (DL) and contrast enhanced magnetic resonance imaging (CE-MRI) images.
methods58 patients (25 males; 28~96 years old) with newly diagnosed lower extremity DVT were recruited. CE-MRI was acquired on a 1.5 T system. The ground truth (GT) of DVT lesions was manually contoured. A DL network with an encoder-decoder architecture was designed for DVT segmentation. 8-Fold cross-validation strategy was applied for training and testing. Dice similarity coefficient (DSC) was adopted to evaluate the network's performance.
resultsIt took about 1.5s for our CNN model to perform the segmentation task in a slice of MRI image. The mean DSC of 58 patients was 0.74± 0.17 and the median DSC was 0.79. Compared with other DL models, our CNN model achieved better performance in DVT segmentation (0.74± 0.17 versus 0.66±0.15, 0.55±0.20, and 0.57±0.22).
conclusionOur proposed DL method was effective and fast for fully automatic segmentation of lower extremity DVT.
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