A Scalable Platform for Exploring and Analyzing Whole Brain Tissue Cleared Images

摘要 在光片荧光显微镜(LSFM)图像中准确定位和表征细胞能力是 对于了解整个大脑的三维结构来说，这是必不可少的。在……里面 我们之前的工作，我们已经成功地开发了一种二维核分割方法，用于核清除 使用深度学习技术的显微图像。尽管卷积神经网络显示出很好的前景 在分割LSFM图像中的细胞时，我们以前的工作局限于二维分割场景，并且受到了影响 来自有限数量的注释数据。在这个项目中，我们的目标是开发一种高通量的3D单元 分词引擎，重点提高了分词的准确性和通用性。首先，我们将 利用虚拟现实(VR)和人群的优势，开发了一个基于云的半自动标注平台 采购。VR中用户友好的注释环境和立体视图可以显著提高 人工标注的效率。我们设计了一个半自动的标注工作流，大大减少了人工 干预，从而提高了注释的准确性和跨不同用户的可复制性。 在公民科学精神的启发下，我们将把注释软件扩展为一个众包平台 这使得我们可以在短时间内获得大量的手动注释。第二，我们将发展一个全面的 使用用3D注释样本训练的3D卷积神经网络的3D细胞分割引擎。 由于获取各向同性LSFM图像往往很困难，我们将进一步开发一种超分辨率方法来 输入高分辨率的图像，便于三维细胞分割。第三，我们将开展迁移学习 框架，使我们的3D细胞分割引擎足够通用，以应用于新的LSFM数据，该数据 由于成像设置或清除/染色方案的不同，图像外观可能存在显著差异。这 通用框架将允许我们为新的LSFM数据快速开发特定的细胞分割解决方案 很少甚至没有手动注解，通过转移现有的3D分割引擎已经 用足够数量的带注释的样本进行训练。第四，我们将把我们的计算工具应用于几个 先导性神经科学研究：(1)研究拓扑异构酶I(自闭症的一种与转录相关的 调节器)调节大脑结构，以及(2)研究发育过程中遗传对细胞类型的影响 通过量化胎儿皮质组织中的祖细胞数量来研究人类大脑。成功实施我们的 该项目将在神经科学界对完整细胞的可视化和分析产生广泛影响 健康和疾病大脑中单个细胞类型的分辨率图。改进的细胞分割引擎 3D技术允许来自世界各地的科学家准确而高效地共享和处理彼此的数据， 从而提高了重复性和动力性。

Abstract The ability of accurate localize and characterize cells in light sheet fluorescence microscopy (LSFM) image is indispensable for shedding new light on the understanding of three dimensional structures of the whole brain. In our previous work, we have successfully developed a 2D nuclear segmentation method for the nuclear cleared microscopy images using deep learning techniques. Although the convolutional neural networks show promise in segmenting cells in LSFM images, our previous work is confined in 2D segmentation scenario and suffers from the limited number of annotated data. In this project, we aim to develop a high throughput 3D cell segmentation engine, with the focus on improving the segmentation accuracy and generality. First, we will develop a cloud based semi-automatic annotation platform using the strength of virtual reality (VR) and crowd sourcing. The user-friendly annotation environment and stereoscopic view in VR can significantly improve the efficiency of manual annotation. We design a semi-automatic annotation workflow to largely reduce human intervention, and thus improve both the accuracy and the replicability of annotation across different users. Enlightened by the spirit of citizen science, we will extend the annotation software into a crowd sourcing platform which allows us to obtain a massive number of manual annotations in short time. Second, we will develop a fully 3D cell segmentation engine using 3D convolutional neural networks trained with the 3D annotated samples. Since it is often difficult to acquire isotropic LSFM images, we will further develop a super resolution method to impute a high resolution image to facilitate the 3D cell segmentation. Third, we will develop a transfer learning framework to make our 3D cell segmentation engine general enough to the application of novel LSFM data which might have significant gap of image appearance due to different imaging setup or clearing/staining protocol. This general framework will allow us to rapidly develop a specific cell segmentation solution for new LSFM data with very few or even no manual annotations, by transferring the existing 3D segmentation engine that has been trained with a sufficient number of annotated samples. Fourth, we will apply our computational tools to several pilot neuroscience studies: (1) Investigating how topoisomerase I (one of the autism linked transcriptional regulators) regulates brain structure, and (2) Investigating genetic influence on cell types in the developing human brain by quantifying the number of progenitor cells in fetal cortical tissue. Successful carrying out our project will have wide-reaching impact in neuroscience community in visualizing and analyzing complete cellular resolution maps of individual cell types within healthy and disease brain. The improved cell segmentation engine in 3D allows scientists from all over the world to share and process each other’s data accurately and efficiently, thus increasing reproducibility and power.