Chen Cheng, Zhou Kangneng, Lu Tong, Ning Huansheng, Xiao Ruoxiu
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Visual 3D Medical Science and Technology Development, Co. Ltd, Beijing 100082, China.
Comput Methods Programs Biomed. 2023 May;233:107475. doi: 10.1016/j.cmpb.2023.107475. Epub 2023 Mar 11.
Cerebrovascular segmentation from time-of-flight magnetic resonance angiography (TOF-MRA) is important but challenging for the simulation and measurement of cerebrovascular diseases. Recently, deep learning has promoted the rapid development of cerebrovascular segmentation. However, model optimization relies on voxel or regional punishment and lacks global awareness and interpretation from the texture and edge. To overcome the limitations of the existing methods, we propose a new cerebrovascular segmentation method to obtain more refined structures.
In this paper, we propose a new adversarial model that achieves segmentation using segmentation model and filters the results using discriminator. Considering the sample imbalance in cerebrovascular imaging, we separated the TOF-MRA images and utilized high- and low-frequency images to enhance the texture and edge representation. The encoder weight sharing from the segmentation model not only saves the model parameters, but also strengthens the integration and separation correlation. Diversified discrimination enhances the robustness and regularization of the model.
The adversarial model was tested using two cerebrovascular datasets. It scored 82.26% and 73.38%, respectively, ranking first on both datasets. The results show that our method not only outperforms the recent cerebrovascular segmentation model, but also surpasses the common adversarial models.
Our adversarial model focuses on improving the extraction ability of the model on texture and edge, thereby achieving awareness of the global cerebrovascular topology. Therefore, we obtained an accurate and robust cerebrovascular segmentation. This framework has potential applications in many imaging fields, particularly in the application of sample imbalance. Our code is available at the website https://github.com/MontaEllis/ISA-model.
从时间飞跃磁共振血管造影（TOF-MRA）中进行脑血管分割对于脑血管疾病的模拟和测量而言很重要，但具有挑战性。近年来，深度学习推动了脑血管分割的快速发展。然而，模型优化依赖于体素或区域惩罚，缺乏对纹理和边缘的全局感知及解释。为克服现有方法的局限性，我们提出一种新的脑血管分割方法以获得更精细的结构。
在本文中，我们提出一种新的对抗模型，该模型使用分割模型实现分割，并使用鉴别器对结果进行过滤。考虑到脑血管成像中的样本不均衡问题，我们将TOF-MRA图像分开，并利用高频和低频图像来增强纹理和边缘表示。分割模型的编码器权重共享不仅节省了模型参数，还加强了整合与分离的相关性。多样化的判别增强了模型的鲁棒性和正则化。
使用两个脑血管数据集对对抗模型进行了测试。它分别获得了82.26%和73.38%的分数，在两个数据集上均排名第一。结果表明，我们的方法不仅优于最近的脑血管分割模型，而且超过了常见的对抗模型。
我们的对抗模型专注于提高模型对纹理和边缘的提取能力，从而实现对全局脑血管拓扑结构的感知。因此，我们获得了准确且鲁棒的脑血管分割。该框架在许多成像领域具有潜在应用，特别是在样本不均衡的应用中。我们的代码可在网站https://github.com/MontaEllis/ISA-model上获取。
