Software for interactive segmentation of the carotid artery from 3D black blood magnetic resonance images

2004 ◽  
Vol 75 (1) ◽  
pp. 31-43 ◽  
Author(s):  
Yuan Jin ◽  
Hanif M. Ladak
Keyword(s):  
Magnetic Resonance ◽  
Carotid Artery ◽  
Magnetic Resonance Images ◽  
Black Blood ◽  
Interactive Segmentation

scholarly journals Deep Learning for Accurate Segmentation of Venous Thrombus from Black-Blood Magnetic Resonance Images: A Multicenter Study

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Chuanqi Sun ◽  
Xiangyu Xiong ◽  
Tianjing Zhang ◽  
Xiuhong Guan ◽  
Huan Mao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Magnetic Resonance ◽  
Ground Truth ◽  
Magnetic Resonance Images ◽  
Black Blood ◽  
Mr Images ◽  
Vessel Segment ◽  
Learning Network ◽  
Venous Thrombus ◽  
Deep Learning Network

Objective. Deep vein thrombosis (DVT) is the third-largest cardiovascular disease, and accurate segmentation of venous thrombus from the black-blood magnetic resonance (MR) images can provide additional information for personalized DVT treatment planning. Therefore, a deep learning network is proposed to automatically segment venous thrombus with high accuracy and reliability. Methods. In order to train, test, and external test the developed network, total images of 110 subjects are obtained from three different centers with two different black-blood MR techniques (i.e., DANTE-SPACE and DANTE-FLASH). Two experienced radiologists manually contoured each venous thrombus, followed by reediting, to create the ground truth. 5-fold cross-validation strategy is applied for training and testing. The segmentation performance is measured on pixel and vessel segment levels. For the pixel level, the dice similarity coefficient (DSC), average Hausdorff distance (AHD), and absolute volume difference (AVD) of segmented thrombus are calculated. For the vessel segment level, the sensitivity (SE), specificity (SP), accuracy (ACC), and positive and negative predictive values (PPV and NPV) are used. Results. The proposed network generates segmentation results in good agreement with the ground truth. Based on the pixel level, the proposed network achieves excellent results on testing and the other two external testing sets, DSC are 0.76, 0.76, and 0.73, AHD (mm) are 4.11, 6.45, and 6.49, and AVD are 0.16, 0.18, and 0.22. On the vessel segment level, SE are 0.95, 0.93, and 0.81, SP are 0.97, 0.92, and 0.97, ACC are 0.96, 0.94, and 0.95, PPV are 0.97, 0.82, and 0.96, and NPV are 0.97, 0.96, and 0.94. Conclusions. The proposed deep learning network is effective and stable for fully automatic segmentation of venous thrombus on black blood MR images.

scholarly journals Using a distance map and an active contour model to segment the carotid artery boundary from the lumen contour in proton density weighted magnetic resonance images

2020 ◽  
Vol 123 ◽  
pp. 103901
Author(s):  
Danilo Samuel Jodas ◽  
Maria Francisca Monteiro da Costa ◽  
Tiago A.A. Parreira ◽  
Aledir Silveira Pereira ◽  
João Manuel R.S. Tavares
Keyword(s):  
Magnetic Resonance ◽  
Carotid Artery ◽  
Active Contour ◽  
Active Contour Model ◽  
Magnetic Resonance Images ◽  
Proton Density ◽  
Distance Map ◽  
Contour Model

scholarly journals Lumen segmentation in magnetic resonance images of the carotid artery

2016 ◽  
Vol 79 ◽  
pp. 233-242 ◽  
Author(s):  
Danilo Samuel Jodas ◽  
Aledir Silveira Pereira ◽  
João Manuel R.S. Tavares
Keyword(s):  
Magnetic Resonance ◽  
Carotid Artery ◽  
Magnetic Resonance Images ◽  
Lumen Segmentation
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