Li Yuxin, Gong Hui, Yang Xiaoquan, Yuan Jing, Jiang Tao, Li Xiangning, Sun Qingtao, Zhu Dan, Wang Zhenyu, Luo Qingming, Li Anan
Collaborative Innovation Center for Biomedical Engineering, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and TechnologyWuhan, China.
Britton Chance Center and MOE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and TechnologyWuhan, China.
Front Neural Circuits. 2017 Jul 31;11:51. doi: 10.3389/fncir.2017.00051. eCollection 2017.
Three-dimensional imaging of whole mammalian brains at single-neuron resolution has generated terabyte (TB)- and even petabyte (PB)-sized datasets. Due to their size, processing these massive image datasets can be hindered by the computer hardware and software typically found in biological laboratories. To fill this gap, we have developed an efficient platform named TDat, which adopts a novel data reformatting strategy by reading cuboid data and employing parallel computing. In data reformatting, TDat is more efficient than any other software. In data accessing, we adopted parallelization to fully explore the capability for data transmission in computers. We applied TDat in large-volume data rigid registration and neuron tracing in whole-brain data with single-neuron resolution, which has never been demonstrated in other studies. We also showed its compatibility with various computing platforms, image processing software and imaging systems.
以单神经元分辨率对整个哺乳动物大脑进行三维成像已生成了数TB甚至数PB大小的数据集。由于其规模巨大，处理这些海量图像数据集可能会受到生物实验室中常见的计算机硬件和软件的阻碍。为了填补这一空白，我们开发了一个名为TDat的高效平台，该平台通过读取长方体数据并采用并行计算，采用了一种新颖的数据重新格式化策略。在数据重新格式化方面，TDat比任何其他软件都更高效。在数据访问方面，我们采用并行化来充分挖掘计算机中的数据传输能力。我们将TDat应用于具有单神经元分辨率的全脑数据的大规模数据刚性配准和神经元追踪，这在其他研究中从未得到证明。我们还展示了它与各种计算平台、图像处理软件和成像系统的兼容性。
