Fully Automated Segmentation of Lower Extremity Deep Vein Thrombosis Using Convolutional Neural Network.
Biomed Res Int
; 2019: 3401683, 2019.
Article
in En
| MEDLINE
| ID: mdl-31281832
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Image Processing, Computer-Assisted
/
Neural Networks, Computer
/
Venous Thrombosis
/
Lower Extremity
Limits:
Adult
/
Aged
/
Aged80
/
Female
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Humans
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Male
/
Middle aged
Language:
En
Journal:
Biomed Res Int
Year:
2019
Document type:
Article
Affiliation country: