Recently there has been an increasing trend to use deep learning frameworks
for both 2D consumer images and for 3D medical images. However, there has been
little effort to use deep frameworks for volumetric vascular segmentation. We
wanted to address this by providing a freely available dataset of 12 annotated
two-photon vasculature microscopy stacks. We demonstrated the use of deep
learning framework consisting both 2D and 3D convolutional filters (ConvNet).
Our hybrid 2D-3D architecture produced promising segmentation result. We
derived the architectures from Lee et al. who used the ZNN framework initially
designed for electron microscope image segmentation. We hope that by sharing
our volumetric vasculature datasets, we will inspire other researchers to
experiment with vasculature dataset and improve the used network architectures.

本研究旨在为自由使用的 2 光子血管显微镜成像数据集提供一种深度学习框架，该框架基于 2D 和 3D 卷积滤波器 (ConvNet)。 这种 2D 和 3D 混合体构架的分割结果表明，这对于分割体积性血管成像是有很好的推广潜力的。