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

摘要 利用片层荧光显微镜（LSFM）图像对细胞进行精确定位和表征的能力， 这对于理解整个大脑的三维结构是不可或缺的。在 我们以前的工作，我们已经成功地开发了一个2D核分割方法的核清除 显微镜图像使用深度学习技术。尽管卷积神经网络显示出了希望， 在分割LSFM图像中的细胞时，我们以前的工作局限于2D分割场景， 从有限的注释数据中。在这个项目中，我们的目标是开发一个高通量的3D细胞， 分割引擎，重点是提高分割的准确性和通用性。一是 利用虚拟现实（VR）和人群的优势，开发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着提高 手动注释的效率。我们设计了一个半自动的注释工作流程，以大大减少人工 这可以减少干预，从而提高不同用户之间注释的准确性和可复制性。 在公民科学精神的启发下，我们将标注软件扩展为众包平台 这允许我们在短时间内获得大量的手动注释。第二，我们将全面发展 3D细胞分割引擎使用3D卷积神经网络，用3D注释样本训练。 由于获取各向同性LSFM图像通常很困难，我们将进一步开发一种超分辨率方法， 输入高分辨率图像以促进3D细胞分割。第三，我们将发展迁移学习 框架，使我们的3D细胞分割引擎足够通用的新的LSFM数据的应用， 可能由于不同的成像设置或清洁/染色方案而导致图像外观存在显著差距。这 通用框架将使我们能够快速开发新的LSFM数据的特定细胞分割解决方案， 很少甚至没有手动注释，通过转移现有的3D分割引擎， 用足够数量的注释样本进行训练。第四，我们将把我们的计算工具应用于几个 初步神经科学研究：（1）研究拓扑异构酶I（自闭症相关转录因子之一） 调节剂）调节大脑结构，和（2）研究遗传对发育中细胞类型的影响。 通过定量胎儿皮质组织中祖细胞的数量来研究人脑。成功执行我们的 该项目将在神经科学界产生广泛的影响，在可视化和分析完整的细胞 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技术使世界各地的科学家能够准确、高效地共享和处理彼此的数据， 从而增加再现性和功率。

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.