使用交互式体素网格变形和渲染的多模态3D图像配准

T. Richard, Yan Chastagnier, V. Szabo, K. Chalard, B. Summa, Jean-Marc Thiery, T. Boubekeur, Noura Faraj
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引用次数: 0

摘要

提出了一种基于三维用户引导的体形和强度变形的多模态三维图像配准框架。能够在3D中应用变形,可以访问广泛的新交互范围,允许从任何采集方法和任何器官,完整或部分的图像注册。我们的框架使用最先进的3D体绘制方法来实时反馈配准精度以及图像变形。我们提出了一种新的方法变化来准确显示3D分割体素网格,这是在可视化分割地图集的注册环境中所需要的。我们的流水线是在开源软件中实现的(可通过GitHub获得),生物学家直接使用它在Allen brain Atlas上注册小鼠脑模型自身荧光采集。后一种映射允许他们检索在获得的大脑数据集的分割图谱上正确识别的感兴趣的区域,因此只提取这些区域的高分辨率图像,避免创建太大的图像
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Multi-modal 3D Image Registration Using Interactive Voxel Grid Deformation and Rendering
We introduce a novel multi-modal 3D image registration framework based on 3D user-guided deformation of both volume’s shape and intensity values. Being able to apply deformations in 3D gives access to a wide new range of interactions allowing for the registration of images from any acquisition method and of any organ, complete or partial. Our framework uses a state of the art 3D volume rendering method for real-time feedback on the registration accuracy as well as the image deformation. We propose a novel methodological variation to accurately display 3D segmented voxel grids, which is a requirement in a registration context for visualizing a segmented atlas. Our pipeline is implemented in an open-source software (available via GitHub) and was directly used by biologists for registration of mouse brain model autofluorescence acquisition on the Allen Brain Atlas. The latter mapping allows them to retrieve regions of interest properly identified on the segmented atlas in acquired brain datasets and therefore extract only high-resolution images of those areas, avoiding the creation of images too large
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