適用於果蠅腦中蕈狀體的三維對位研究

3D Registration for Mushroom Bodies of Drosophila Brain

扈安祺(An-Chi Hu)

指導教授 ： 陳永昌

國立清華大學/電機資訊學院/電機工程學系/碩士(2006年)

https://doi.org/10.6843/NTHU.2006.00126

摘要

由於果蠅容易培養且具有學習與記憶的能力，果蠅在腦科學的研究中扮演重要的角色，科學家藉由了解果蠅腦這樣比較簡單的結構，去推測複雜的人腦的功能與結構。果蠅腦中的蕈狀體更是和果蠅學習與記憶的能力息息相關。因此，對於果蠅腦中蕈狀體的研究是重要的。
此外，果蠅腦中蕈狀體的形狀是非常特別的，所以對於蕈狀體的對位研究就成為了一個有用且有趣的課題。此篇論文中，我們針對果蠅腦中的蕈狀體提出了一個自動化對位的系統，把一個已知的果蠅腦中蕈狀體的標準模型對位至用共軛焦顯微鏡成像的蕈狀體的整組切片影像。這樣的影像通常會有許多不要的部份，那是由於染色的時候沒有辦法只單獨染到蕈狀體。而這個系統必須對位至真正的蕈狀體的邊界。
這篇論文中提出主要以表面為基礎的雙層架構的對位演算法，由整體的對位和局部的對位組成。利用仿射轉換來減少比較大的且較為整體的差異，例如:兩個要對位的表面模型的角度和大小。接著利用局部的對位來消除比較細微的差異。首先利用以變形場為基礎的變形方式，來消除兩個模型中蕈狀體的六個軸的角度和長度的差異。接著再利用以控制點為基礎的變形來做更細微的調整。實驗結果顯示，這個演算法可以將已知的果蠅腦中的蕈狀體的標準表面模型對位至共軛焦顯微鏡成像的整組切片影像中，且有不錯的效果。

關鍵字

對位 ； 變形

並列摘要

In the research of life science, a fruit fly, Drosophila melanogaster, with the abilities of learning and memory is chosen for research to facilitate the understanding of structures and functions of the brain neural network. In fruit flies’ brains, the mushroom bodies are important neuropils, known to be involved in learning and memory.
Due to the importance of mushroom bodies and its characteristic shape, registration for the mushroom bodies is an interesting and useful research topic. The system we developed in this thesis provides an automatic registration applied to mushroom bodies. We register the standard model of mushroom bodies to individual data set of confocal images with noises.
A mainly two-stage surface-based registration algorithm has been proposed in this study. This algorithm is composed of global and local registration. Affine registration has been chosen as the global registration to eliminate the large, global differences such as scale and orientation between the two surface models. And a two-level local registration has been proposed to eliminate the remaining differences between the surfaces after the global registration. The two-level local registration initially first eliminates the angular differences and the length differences of the six axes between the target model and the standard model. Then, performing the point-based warping eliminates finer variations between the two models. The experimental results show that performing the registration algorithm proposed here makes the volume data registered satisfactorily roughly well.

並列關鍵字

registration ； warping

參考文獻

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