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• W2948446542 abstract "Objective . Deep 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. Methods . 58 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. Results . It 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). Conclusion . Our proposed DL method was effective and fast for fully automatic segmentation of lower extremity DVT." @default.
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• W2948446542 date "2019-06-09" @default.
• W2948446542 modified "2023-10-17" @default.
• W2948446542 title "Fully Automated Segmentation of Lower Extremity Deep Vein Thrombosis Using Convolutional Neural Network" @default.
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• W2948446542 doi "https://doi.org/10.1155/2019/3401683" @default.
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