Vessel Segmentation Model using Automated Threshold Algorithm from Lower Leg MRI

Lee, Ji Young; Mun, Jin Yeong; Taheri, Mohammad; Shin, Sung Y.

doi:10.1145/3129676.3129701

Cited by 5 publications

(1 citation statement)

References 21 publications

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“…To date, no studies about automatic segmentation of DVT have been reported. Some automatic segmentation methods have been applied to lower limb blood vessel segmentation [12, 13], such as fuzzy connected object delineation algorithms [14], k-means [15], fuzzy c-means clustering [16], and convolutional neural network (CNN) [17]. Among these automated methods, machine learning (ML), especially deep learning (DL), has shown great potential [18].…”

Section: Introductionmentioning

confidence: 99%

Fully Automated Segmentation of Lower Extremity Deep Vein Thrombosis Using Convolutional Neural Network

Huang

Tian

Yuan

et al. 2019

BioMed Research International

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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.

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