Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi'an University of Technology, Xi'an 710048, China.
HUST-Suzhou Institute for Brainsmatics, Suzhou 215123, China.
Bioinformatics. 2023 Apr 3;39(4). doi: 10.1093/bioinformatics/btad145.
Reconstructing and analyzing all blood vessels throughout the brain is significant for understanding brain function, revealing the mechanisms of brain disease, and mapping the whole-brain vascular atlas. Vessel segmentation is a fundamental step in reconstruction and analysis. The whole-brain optical microscopic imaging method enables the acquisition of whole-brain vessel images at the capillary resolution. Due to the massive amount of data and the complex vascular features generated by high-resolution whole-brain imaging, achieving rapid and accurate segmentation of whole-brain vasculature becomes a challenge.
We introduce HP-VSP, a high-performance vessel segmentation pipeline based on deep learning. The pipeline consists of three processes: data blocking, block prediction, and block fusion. We used parallel computing to parallelize this pipeline to improve the efficiency of whole-brain vessel segmentation. We also designed a lightweight deep neural network based on multi-resolution vessel feature extraction to segment vessels at different scales throughout the brain accurately. We validated our approach on whole-brain vascular data from three transgenic mice collected by HD-fMOST. The results show that our proposed segmentation network achieves the state-of-the-art level under various evaluation metrics. In contrast, the parameters of the network are only 1% of those of similar networks. The established segmentation pipeline could be used on various computing platforms and complete the whole-brain vessel segmentation in 3 h. We also demonstrated that our pipeline could be applied to the vascular analysis.
The dataset is available at http://atlas.brainsmatics.org/a/li2301. The source code is freely available at https://github.com/visionlyx/HP-VSP.
重建和分析大脑中的所有血管对于理解大脑功能、揭示大脑疾病的机制以及绘制全脑血管图谱都具有重要意义。血管分割是重建和分析的基本步骤。全脑光学显微镜成像方法能够以毛细血管分辨率获取全脑血管图像。由于高分辨率全脑成像产生的大量数据和复杂的血管特征，实现全脑血管的快速准确分割成为一项挑战。
我们引入了 HP-VSP，这是一种基于深度学习的高性能血管分割管道。该管道由三个过程组成：数据块化、块预测和块融合。我们使用并行计算将该管道并行化，以提高全脑血管分割的效率。我们还设计了一种基于多分辨率血管特征提取的轻量级深度神经网络，以准确分割大脑不同尺度的血管。我们在由 HD-fMOST 采集的三只转基因小鼠的全脑血管数据上验证了我们的方法。结果表明，我们提出的分割网络在各种评估指标下达到了最先进的水平。相比之下，网络的参数仅为类似网络的 1%。建立的分割管道可以在各种计算平台上使用，并在 3 小时内完成全脑血管分割。我们还证明了我们的管道可以应用于血管分析。
数据集可在 http://atlas.brainsmatics.org/a/li2301 获得。源代码可在 https://github.com/visionlyx/HP-VSP 上免费获得。
