标题： 一种对抗方法用于注册极端分辨率组织透明化3D脑图像 显示英文标题

标题： An Adversarial Approach to Register Extreme Resolution Tissue Cleared 3D Brain Images

摘要： 我们开发了一种基于生成补丁的3D图像配准模型，该模型能够注册通过生物化学过程（即组织透明化）获得的极高分辨率图像。组织透明化过程从组织中去除脂质和脂肪，使组织变得透明。当使用光片荧光显微镜对透明化的组织进行成像时，所得到的图像为观察组织内部的细胞活动和动态提供了清晰的窗口。因此，这些图像富含细胞信息且分辨率极高（例如2560x2160x676）。分析这种高分辨率的图像是任何图像分析流程中的一个难题。当需要比较图像时，图像配准是图像分析流程中的常见步骤。传统的图像配准方法无法处理这种程度的图像。在本文中，我们通过提出一种名为InvGAN的基于补丁的生成网络来解决这一超高分辨率图像配准问题。我们提出的网络可以注册极高分辨率的透明化组织图像。本文中使用的透明化组织数据集来自名为CUBIC的组织透明化协议。我们将我们的方法与传统及基于深度学习的配准方法进行了对比。使用了两种不同版本的CUBIC数据集，分别代表两种不同的分辨率，即25%和100%。在两种不同分辨率下的实验清楚地展示了分辨率对配准质量的影响。在25%分辨率下，我们的方法以极短的时间（大约7分钟）实现了可比的配准精度。在100%分辨率下，除了Elastix配准工具外，大多数传统配准方法都失败了。Elastix需要28小时进行配准，而我们提出的InvGAN仅需10分钟。 显示英文摘要

摘要： We developed a generative patch based 3D image registration model that can register very high resolution images obtained from a biochemical process name tissue clearing. Tissue clearing process removes lipids and fats from the tissue and make the tissue transparent. When cleared tissues are imaged with Light-sheet fluorescent microscopy, the resulting images give a clear window to the cellular activities and dynamics inside the tissue.Thus the images obtained are very rich with cellular information and hence their resolution is extremely high (eg .2560x2160x676). Analyzing images with such high resolution is a difficult task for any image analysis pipeline.Image registration is a common step in image analysis pipeline when comparison between images are required. Traditional image registration methods fail to register images with such extant. In this paper we addressed this very high resolution image registration issue by proposing a patch-based generative network named InvGAN. Our proposed network can register very high resolution tissue cleared images. The tissue cleared dataset used in this paper are obtained from a tissue clearing protocol named CUBIC. We compared our method both with traditional and deep-learning based registration methods.Two different versions of CUBIC dataset are used, representing two different resolutions 25% and 100% respectively. Experiments on two different resolutions clearly show the impact of resolution on the registration quality. At 25% resolution, our method achieves comparable registration accuracy with very short time (7 minutes approximately). At 100% resolution, most of the traditional registration methods fail except Elastix registration tool.Elastix takes 28 hours to register where proposed InvGAN takes only 10 minutes.