本文選題：腦成像數(shù)據(jù)　切入點(diǎn)：形狀重建　出處：《華中科技大學(xué)》2016年博士論文　論文類型：學(xué)位論文

【摘要】：腦是自然界最復(fù)雜的系統(tǒng)之一,支配著人類的思維與行為。研究腦的結(jié)構(gòu)和功能,對描述智力、思維和意識的產(chǎn)生機(jī)制,揭示腦的工作原理具有重要意義,進(jìn)一步能夠促進(jìn)我們對一些高級功能和神經(jīng)精神類疾病形成機(jī)制的理解。隨著生命科學(xué)、光學(xué)、機(jī)械和信息科學(xué)等多學(xué)科的交叉和融合發(fā)展,使得在高分辨率水平獲取小鼠全腦的數(shù)據(jù)集成為可能。將獲取的腦成像數(shù)據(jù)轉(zhuǎn)化為生物學(xué)知識已成為腦研究的瓶頸問題。作為腦研究的重要部分,腦成像數(shù)據(jù)的形狀重建,在數(shù)字化重建神經(jīng)元、腦成像數(shù)據(jù)配準(zhǔn)、定性定量分析等方面具有重要的意義,為研究神經(jīng)元形態(tài)、定量分析神經(jīng)元的投射等提供幫助,為探索腦疾病的形成機(jī)理奠定基礎(chǔ)。然而,由于大的數(shù)據(jù)量以及高的數(shù)據(jù)復(fù)雜度,使得腦成像數(shù)據(jù)的形狀重建面臨巨大的挑戰(zhàn)。圍繞腦成像數(shù)據(jù)轉(zhuǎn)化為生物學(xué)知識的需求,針對其中形狀重建這一具體問題,本文通過構(gòu)建新型變分模型的方式來進(jìn)行腦成像數(shù)據(jù)的形狀重建,主要貢獻(xiàn)如下：(1)構(gòu)建了新型的變分模型。在普通變分模型的基礎(chǔ)上,本文通過在發(fā)射射線采樣的信號中給出能量方程的方式構(gòu)建了新型的變分模型。該模型能夠控制邊界元的演化方向,解決了普通變分模型在形狀重建過程中對初始輪廓的選取比較敏感這一問題;同時,通過控制邊界元的演化方向和光滑約束項(xiàng)的引入,解決了噪聲信號干擾的問題。(2)基于球坐標(biāo)系的變分模型,建立了重建神經(jīng)元胞體形態(tài)的方法。該方法將三維圖像信號轉(zhuǎn)換到球坐標(biāo)系中,在球坐標(biāo)系下構(gòu)建一個變分模型來重建胞體形態(tài)。通過對實(shí)際數(shù)據(jù)的測試,驗(yàn)證了基于球坐標(biāo)系的變分模型具有重建密集、粗突起干擾等情形下的神經(jīng)元胞體形態(tài)的能力。(3)基于重采樣的變分模型,建立了重建鼠腦輪廓的方法。該方法通過重采樣的方式獲得輪廓附近的局部信號,在重采樣的數(shù)據(jù)中構(gòu)建一個變分模型來重建鼠腦輪廓。通過對實(shí)際數(shù)據(jù)集的測試,驗(yàn)證了基于重采樣的變分模型具有重建數(shù)據(jù)量大、邊界信號不均勻干擾等情形下的鼠腦輪廓形態(tài)的能力。本文建立的方法在神經(jīng)元追蹤、數(shù)據(jù)預(yù)處理、鼠腦配準(zhǔn)等方面具有實(shí)際的應(yīng)用。重建神經(jīng)元胞體形態(tài)的方法已經(jīng)應(yīng)用于密集神經(jīng)群落和稀疏神經(jīng)元的追蹤方面,通過重建神經(jīng)元胞體形態(tài),獲得與胞體直接相連的樹突與軸突的相關(guān)信息,為神經(jīng)纖維的追蹤和神經(jīng)網(wǎng)絡(luò)的分配提供先驗(yàn)信息;重建鼠腦輪廓的方法已經(jīng)應(yīng)用于數(shù)據(jù)預(yù)處理、鼠腦配準(zhǔn)等方面,通過獲得的腦輪廓,可以去除鼠腦外的干擾信息,使得數(shù)據(jù)預(yù)處理、鼠腦配準(zhǔn)更為準(zhǔn)確。
[Abstract]:Brain is one of the most complex systems in nature, which dominates human thinking and behavior. It is of great significance to study the structure and function of brain to describe the mechanism of intelligence, thinking and consciousness, and to reveal the working principle of brain. It can further promote our understanding of the formation mechanism of some advanced functional and neuropsychiatric diseases. With the development of life science, optics, machinery and information science, the interdisciplinary and integration of many subjects, such as life science, optics, machinery and information science, It is possible to obtain the whole brain data of mice at high resolution level. Converting the obtained brain imaging data into biological knowledge has become the bottleneck of brain research. As an important part of brain research, the shape reconstruction of brain imaging data, It is of great significance in the digital reconstruction of neurons, the registration of brain imaging data, the qualitative and quantitative analysis and so on, which can be helpful for the study of neuron morphology and the quantitative analysis of neuronal projection. However, because of the large amount of data and high data complexity, the shape reconstruction of brain imaging data faces a great challenge. Aiming at the specific problem of shape reconstruction, this paper constructs a new variational model to reconstruct the shape of brain imaging data. The main contributions are as follows: 1) A new variational model is constructed. In this paper, a new variational model is constructed by giving the energy equation in the radially sampled signal, which can control the evolution direction of the boundary element. The problem that the general variational model is sensitive to the selection of the initial contour in the shape reconstruction process is solved. At the same time, by controlling the evolution direction of the boundary element and the introduction of the smooth constraint term, The problem of noise signal interference is solved. Based on the variational model of spherical coordinate system, a method of reconstructing neuronal cell body morphology is established, which converts 3D image signal to spherical coordinate system. A variational model is constructed in spherical coordinate system to reconstruct the shape of the cell body. Through the test of the actual data, it is proved that the variational model based on the spherical coordinate system has dense reconstruction. Based on the variational model of resampling, a method for reconstructing the contours of the brain is established. The local signals near the contours are obtained by resampling. A variational model is constructed in resampling data to reconstruct the contours of the brain. By testing the actual data set, it is proved that the variational model based on resampling has a large amount of reconstructed data. The ability of brain contours in the presence of inhomogeneous boundary signal interference. The proposed method is used in neuronal tracking, data preprocessing, and so on. The method of reconstructing neuronal somatic morphology has been applied to the tracing of dense nerve communities and sparse neurons by reconstructing neuronal somatic morphology. To obtain the related information of dendrites and axons directly connected to the cell body, to provide a priori information for the tracking of nerve fibers and the distribution of neural networks, the method of reconstructing the contours of the brain has been applied in data preprocessing, registration of the brain, and so on. Through the obtained brain contour, the interference information outside the brain can be removed, which makes the data preprocessing and the brain registration more accurate.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：R338;TN911.7

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