In:
BioMed Research International, Hindawi Limited, Vol. 2019 ( 2019-06-09), p. 1-7
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.
Type of Medium:
Online Resource
ISSN:
2314-6133
,
2314-6141
DOI:
10.1155/2019/3401683
Language:
English
Publisher:
Hindawi Limited
Publication Date:
2019
detail.hit.zdb_id:
2698540-8
