A nested parallel multiscale convolution for cerebrovascular segmentation

2021 ◽  
Author(s):  
Likun Xia ◽  
Yixuan Xie ◽  
Qiwang Wang ◽  
Hao Zhang ◽  
Cheng He ◽  
...  
Keyword(s):  
Cerebrovascular Segmentation

scholarly journals A novel statistical cerebrovascular segmentation algorithm with particle swarm optimization

2015 ◽  
Vol 148 ◽  
pp. 569-577 ◽  
Author(s):  
Lei Wen ◽  
Xingce Wang ◽  
Zhongke Wu ◽  
Mingquan Zhou ◽  
Jesse S. Jin
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Segmentation Algorithm ◽  
Swarm Optimization ◽  
Cerebrovascular Segmentation

Precise Cerebrovascular Segmentation

2020 ◽  
Author(s):  
F. Taher ◽  
A. Soliman ◽  
H. Kandil ◽  
A. Mahmoud ◽  
A. Shalaby ◽  
...  
Keyword(s):  
Cerebrovascular Segmentation

3D cerebrovascular segmentation combining fuzzy vessel enhancement and level-sets with anisotropic energy weights

2013 ◽  
Vol 31 (2) ◽  
pp. 262-271 ◽  
Author(s):  
Nils Daniel Forkert ◽  
Alexander Schmidt-Richberg ◽  
Jens Fiehler ◽  
Till Illies ◽  
Dietmar Möller ◽  
...  
Keyword(s):  
Level Sets ◽  
Cerebrovascular Segmentation ◽  
Anisotropic Energy ◽  
Vessel Enhancement

scholarly journals Automatic cerebrovascular segmentation methods-a review

2021 ◽  
Vol 10 (3) ◽  
pp. 576
Author(s):  
Fatma Taher ◽  
Neema Prakash
Keyword(s):  
Blood Vessels ◽  
Complex Shape ◽  
Automatic Segmentation ◽  
Cerebrovascular Diseases ◽  
And Topology ◽  
Cerebrovascular Segmentation ◽  
Segmentation Methods ◽  
3D Cnn ◽  
Cerebrovascular System ◽  
The Brain

Cerebrovascular diseases are one of the serious causes for the increase in mortality rate in the world which affect the blood vessels and blood supply to the brain. In order, diagnose and study the abnormalities in the cerebrovascular system, accurate segmentation methods can be used. The shape, direction and distribution of blood vessels can be studied using automatic segmentation. This will help the doctors to envisage the cerebrovascular system. Due to the complex shape and topology, automatic segmentation is still a challenge to the clinicians. In this paper, some of the latest approaches used for segmentation of magnetic resonance angiography images are explained. Some of such methods are deep convolutional neural network (CNN), 3dimentional-CNN (3D-CNN) and 3D U-Net. Finally, these methods are compared for evaluating their performance. 3D U-Net is the better performer among the described methods.

scholarly journals Voxel-Wise Adversarial FiboNet for 3D Cerebrovascular Segmentation on Magnetic Resonance Angiography Images

2021 ◽  
Vol 15 ◽  
Author(s):  
Bin Guo ◽  
Fugen Zhou ◽  
Bo Liu ◽  
Xiangzhi Bai
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Angiography ◽  
Loss Function ◽  
Class Imbalance ◽  
Ground Truth ◽  
Noise Robustness ◽  
Dice Coefficient ◽  
Cerebrovascular Segmentation ◽  
New Feature ◽  
Adversarial Model

Cerebrovascular segmentation is important in various clinical applications, such as surgical planning and computer-aided diagnosis. In order to achieve high segmentation performance, three challenging problems should be taken into consideration: (1) large variations in vascular anatomies and voxel intensities; (2) severe class imbalance between foreground and background voxels; (3) image noise with different magnitudes. Limited accuracy was achieved without considering these challenges in deep learning-based methods for cerebrovascular segmentation. To overcome the limitations, we propose an end-to-end adversarial model called FiboNet-VANGAN. Specifically, our contributions can be summarized as follows: (1) to relieve the first problem mentioned above, a discriminator is proposed to regularize for voxel-wise distribution consistency between the segmentation results and the ground truth; (2) to mitigate the problem of class imbalance, we propose to use the addition of cross-entropy and Dice coefficient as the loss function of the generator. Focal loss is utilized as the loss function of the discriminator; (3) a new feature connection is proposed, based on which a generator called FiboNet is built. By incorporating Dice coefficient in the training of FiboNet, noise robustness can be improved by a large margin. We evaluate our method on a healthy magnetic resonance angiography (MRA) dataset to validate its effectiveness. A brain atrophy MRA dataset is also collected to test the performance of each method on abnormal cases. Results show that the three problems in cerebrovascular segmentation mentioned above can be alleviated and high segmentation accuracy can be achieved on both datasets using our method.

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