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Accurate and Fast 3D Image Reconstruction in Fluorescence Microscopy and Automatic labeling of 3D tissue structures

荧光显微镜中准确快速的 3D 图像重建和 3D 组织结构的自动标记

基本信息

批准号：
17300061
负责人：
KUMAZAWA Itsuo
金额：
$ 9.98万
依托单位：
Tokyo Institute of Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
2005
资助国家：
日本
起止时间：
2005 至 2006
项目状态：
已结题

项目摘要

This project has been aiming to evaluate and improve the new three dimensional reconstruction algorithm developed for Fluorescence Microscopy that has been proven effective for artificially generated ideal data in our previous project. In order to evaluate the effectiveness of the algorithm for actual data observed from tissue structures, we corrected actual data using a fluorescence microscope and have conducted comprehensive evaluation using the data under different conditions by changing noise level and a coupled of optical parameters. Through this evaluation, we found that the new algorithm is sensitive to PSF (Point Spread Function) parameters and, affected by non-transparent parts that contradicts the ideal situation that the deconvolution-based reconstruction theory assumes. We have spent the most of our time to solve this problem and concluded that reconstructed results would not be improved as far as using deconvolution-based-algorithm and it is necessary to develop a method that would not assume transparency for the objects. After the evaluation and adding some improvement, we also tried to develop matching algorithms for three dimensional structures between actual tissue structures observed by Fluorescence Microscopy and artificially synthesized three dimensional structures. We have examined an initial three dimensional shape modeled by a polyhedron would converge to the target shape by iteration procedure using a neural network specifically designed for the purpose.

本项目旨在评估和改进荧光显微镜新开发的三维重建算法，该算法在我们之前的项目中被证明对人工生成理想数据是有效的。为了评估算法对组织结构实际观测数据的有效性，我们使用荧光显微镜对实际数据进行校正，并通过改变噪声级和光学参数耦合，对不同条件下的数据进行综合评价。通过评估，我们发现新算法对PSF （Point Spread Function）参数敏感，并且不受非透明部分的影响，这与基于反卷积的重建理论假设的理想情况相矛盾。我们花了大部分时间来解决这个问题，并得出结论，使用基于反卷积的算法重建结果不会得到改善，有必要开发一种不假设物体透明的方法。在评估和改进之后，我们还尝试开发荧光显微镜观察到的实际组织结构与人工合成的三维结构之间的三维结构匹配算法。我们研究了由多面体建模的初始三维形状将收敛到目标形状，并使用专门设计的神经网络进行迭代。