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

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.

Automatic cerebrovascular segmentation methods-a review

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.

Cerebrovascular Segmentation Based on Hidden Markov Model in Phase-Contrast Angiography Images

Background: Phase-Contrast Angiography (PCA) is an acceptable magnetic resonance imaging method for cerebrovascular diseases diagnosis. However, it is an important and great challenge to accurately extract cerebrovascular structures from PCA images because of the complex vascular structures and large amount of noise. To accomplish this task, this work proposes a cerebrovascular segmentation algorithm based on Local Binary Fitting (LBF) and Hidden Markov Model (HMM), which can accurately extraction features from PCA data. Results: Dice Similarity Coefficient (DSC), False Positive Score (FPN), and False Negative Score (FTN) are defined as metrics to assess this algorithm. Results show this method obtain higher accuracy (74.58%, 4.93%, 24.48%) than compared methods. Conclusion: Based on quantitative results, it appears that the proposed method has a higher accuracy rate compared to other methods. Due to no human correction and has no training process, it performs well on small datasets. Thus, this algorithm can accord with clinical requirements.