我们展示了DeepVesselNet,一种针对使用深度学习在3-D血管造影体积中提取血管树和网络以及相应特征时面临的挑战而量身定制的架构。我们讨论了与完整的3D网络相关的低执行速度和高内存要求的问题,由血管体素的低百分比(<3%)引起的高级不平衡,以及准确注释的3D训练数据的不可用性-并提供解决方案作为DeepVesselNet的构建块。首先,我们制定2-D正交十字线过滤器,利用3-D上下文信息在减少计算负担。第二,我们引入了一类平衡交叉熵损失函数,并进行了假阳性率校正,以处理与现有损失函数相关的高级不平衡和高假阳性率问题。最后,我们使用计算血管生成模型生成一个合成数据集,该模型能够在局部网络结构和拓扑的生理约束下模拟血管树的生长,并将这些数据用于迁移学习.我们展示了在不同空间尺度下的一系列血管造影体积的性能,包括人类大脑的临床MRA数据。以及大鼠大脑的CTA显微镜扫描。我们的结果表明,十字线过滤器的速度提高了23%以上,更低的内存占用,更低的网络复杂性,防止过度拟合和可比的精度,没有不同于完整的3-D滤波器。我们的班级平衡指标对于训练网络至关重要,用合成数据进行迁移学习是一种高效的,健壮,和非常普遍的方法导致一个网络,擅长各种血管造影分割任务。我们观察到,子采样和最大池化层可能会导致涉及体素大小结构的任务性能下降。为此,DeepVesselNet架构不使用任何形式的子采样层,很好地用于血管分割,中心线预测,和分叉检测。我们公开我们的合成训练数据,促进未来的研究,并作为首批用于脑血管树分割和分析的公共数据集之一。
We present DeepVesselNet, an architecture tailored to the challenges faced when extracting vessel trees and networks and corresponding features in 3-D angiographic volumes using deep learning. We discuss the problems of low execution speed and high memory requirements associated with full 3-D networks, high-class imbalance arising from the low percentage (<3%) of vessel voxels, and unavailability of accurately annotated 3-D training data-and offer solutions as the building blocks of DeepVesselNet. First, we formulate 2-D orthogonal cross-hair filters which make use of 3-D context information at a reduced computational burden. Second, we introduce a class balancing cross-entropy loss function with false-positive rate correction to handle the high-class imbalance and high false positive rate problems associated with existing loss functions. Finally, we generate a synthetic dataset using a computational angiogenesis model capable of simulating vascular tree growth under physiological constraints on local network structure and topology and use these data for transfer learning. We demonstrate the performance on a range of angiographic volumes at different spatial scales including clinical MRA data of the human brain, as well as CTA microscopy scans of the rat brain. Our results show that cross-hair filters achieve over 23% improvement in speed, lower memory footprint, lower network complexity which prevents overfitting and comparable accuracy that does not differ from full 3-D filters. Our class balancing metric is crucial for training the network, and transfer learning with synthetic data is an efficient, robust, and very generalizable approach leading to a network that excels in a variety of angiography segmentation tasks. We observe that sub-sampling and max pooling layers may lead to a drop in performance in tasks that involve voxel-sized structures. To this end, the DeepVesselNet architecture does not use any form of sub-sampling layer and works well for vessel segmentation, centerline prediction, and bifurcation detection. We make our synthetic training data publicly available, fostering future research, and serving as one of the first public datasets for brain vessel tree segmentation and analysis.