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的能力是 对于为理解整个大脑的三维结构提供新的启示是必不可少的。在 我们之前的工作中，我们已经成功开发了一种用于核清除的二维核分割方法 使用深度学习技术的显微镜图像。尽管卷积神经网络显示出希望 在LSFM图像中的细胞分割中，我们之前的工作仅限于2D分割场景并受到影响 来自有限数量的注释数据。在这个项目中，我们的目标是开发高通量 3D 细胞 分割引擎，重点提高分割精度和通用性。首先，我们将 利用虚拟现实（VR）和人群的力量开发基于云的半自动注释平台 采购。 VR中人性化的注释环境和立体视图可以显着提高 手动标注的效率。我们设计了半自动注释工作流程，大大减少了人力 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，我们将注释软件延伸为众包平台 这使得我们能够在短时间内获得大量的手动注释。第二，我们将开发一个完整的 3D 细胞分割引擎使用 3D 卷积神经网络，并通过 3D 带注释的样本进行训练。 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来 估算高分辨率图像以促进 3D 细胞分割。第三，我们将开发迁移学习 框架使我们的 3D 细胞分割引擎足够通用，能够应用新颖的 LSFM 数据 由于不同的成像设置或清除/染色方案，图像外观可能存在显着差异。这个 通用框架将使我们能够快速开发针对新 LSFM 数据的特定细胞分割解决方案 通过移植现有的3D分割引擎，很少甚至不需要手动注释 使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到几个方面 试点神经科学研究：(1) 研究拓扑异构酶 I（自闭症相关转录因子之一）如何 (2) 研究遗传对发育中细胞类型的影响 通过量化胎儿皮质组织中祖细胞的数量来研究人脑。成功开展我们的 该项目将在可视化和分析完整细胞方面对神经科学界产生广泛影响 健康和患病大脑内单个细胞类型的分辨率图。改进的细胞分割引擎 3D 允许来自世界各地的科学家准确有效地共享和处理彼此的数据， 从而提高再现性和功效。

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.