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首頁> 外文學位 >Automated detection and time lapse analysis of dendritic spines in laser scanning microscopy images.
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Automated detection and time lapse analysis of dendritic spines in laser scanning microscopy images.

機譯：在激光掃描顯微鏡圖像中自動檢測和分析樹突棘。

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The branches extending from the cell body of neurons, the dendrites, receive more than 90% of the synaptic contacts made into that neuron. In many neurons of the mammalian brain, excitatory synapses involve specialized structures called dendritic spines that protrude from the dendrites and contain the molecules and organelles involved in the postsynaptic processing of the synaptic information. Neuron morphology, as captured in part by the structure of these spines, is illustrative of neuronal function and can be instrumental in better understanding the dysfunction seen in neurodegenerative conditions such as Alzheimer's and Parkinson's disease. Hence researchers have shown great interest in quantitatively studying dendritic spine morphology and density both statically and as a function of time. Such studies are typically carried out through the analysis of data collected from a range of microscopy modalities including confocal laser scanning microscopy (CLSM) and two-photon laser scanning microscopy (2PLSM).;Due to the size and complexity of these data sets, manually analyzing the morphological changes of dendritic spines is very time consuming. In the thesis, we describe robust, automated approaches for dendritic spine detection and measurement that are especially suitable to the batch processing of large neuronal image data sets. Our work is roughly divided into three related components. First, we focus on an image processing pipeline we have developed for the neuroinformatics processing system released from our lab called Neuron Image Quantitator (NeuronIQ), an integrated system for automatic dendrite spine detection, quantification, and analysis. Second, to further improve detection results and solve a collection of related "hard problems" (such as disconnected spine segmentation) faced by existing automatic or semi-automatic methods, a post-processing segmentation algorithm based on a Maximum a Posteriori-orientated Markov random field (MAP-OMRF) is discussed in detail. Finally, we will present an efficient particle filter-based algorithm that is capable of tracking morphological changes in the spines over time. Possible future topics will be discussed at the end of the thesis.
機譯：從神經(jīng)元細胞體（樹突）延伸出來的分支接受了該神經(jīng)元中90％以上的突觸接觸。在哺乳動物大腦的許多神經(jīng)元中，興奮性突觸涉及稱為樹突棘的特殊結(jié)構(gòu)，該結(jié)構(gòu)從樹突突伸出并包含與突觸后信息有關(guān)的分子和細胞器。由這些棘突的結(jié)構(gòu)部分捕獲的神經(jīng)元形態(tài)可說明神經(jīng)元功能，并可有助于更好地了解在神經(jīng)退行性疾?。ㄈ绨柎暮Ｄ喜『团两鹕喜。┲谐霈F(xiàn)的功能障礙。因此，研究人員對靜態(tài)地和隨時間變化的樹突棘形態(tài)和密度的定量研究表現(xiàn)出極大的興趣。此類研究通常是通過分析從各種顯微鏡模式（包括共聚焦激光掃描顯微鏡（CLSM）和雙光子激光掃描顯微鏡（2PLSM））收集的數(shù)據(jù)來進行的；由于這些數(shù)據(jù)集的大小和復雜性，需要手動進行分析樹突棘的形態(tài)變化非常耗時。在本文中，我們描述了用于樹突狀脊柱檢測和測量的強大，自動化的方法，特別適合于大型神經(jīng)元圖像數(shù)據(jù)集的批處理。我們的工作大致分為三個相關(guān)部分。首先，我們專注于為從實驗室發(fā)布的神經(jīng)信息處理系統(tǒng)（稱為Neuron Image Quantitator（NeuronIQ））開發(fā)的圖像處理管道，該系統(tǒng)是用于自動樹突脊柱檢測，量化和分析的集成系統(tǒng)。其次，為進一步改善檢測結(jié)果并解決現(xiàn)有的自動或半自動方法面臨的相關(guān)“難題”（例如脊柱分離不連續(xù)），基于最大后驗取向的馬爾可夫隨機的后處理分割算法字段（MAP-OMRF）進行了詳細討論。最后，我們將提出一種基于粒子過濾器的有效算法，該算法能夠跟蹤脊椎隨時間的形態(tài)變化。論文的結(jié)尾將討論可能的未來主題。

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