為了瞭解神經網路體學，神經科學學家製造出大量神經影像，為了進一步分析這些影像，科學家需要透過一系列的自動化軟體工具來標準化影像之間的差異以利之後的分析。此論文中，我們以果蠅當作模式生物來研究神經網路體。我們實驗室已經累積了28,573筆單一神經影像和大約10,000筆果蠅的驅動影像。首先，我們先設計了一套演算法(NeuroRetriever)能夠大規模且自動地從共軛焦顯微鏡掃描出的螢光影像圈選出單一神經元。這套方法主要是藉由對原始影像做一系列的前處理，在不同的螢光強度閾值的濾鏡下，我們建立了一個高動態範圍的閾值設定法來決定神經結構特徵，且能夠反映出神經元上的神經分支。藉由NeuroRetriever我們成功地從22,037筆原始影像中圈選出28,125筆和人工圈選不相上下的單一神經影像。接下來我們建立了一個收集超過1億筆影像比對結果的資料庫(FlyDriver)，此資料庫包含了單一神經元影像比對驅動影像和驅動影像比對驅動影像。藉由這些結果，資料庫能夠根據使用者所選定的特定神經元，找出專一性表達這些特定神經元的驅動果蠅影像。而那些找不到有任何驅動果蠅表達的特定神經元，我們可以利用驅動影像的連集和交集來產生可能的影像配對，將可能的果蠅配對結果提供給使用者。最後我們提供了一個用來最佳視覺化果蠅神經的演算法(Kaleido)。此方法能夠透過瀏覽器至少同時顯示一萬顆神經，只需要適量的記憶體而沒有增加太多計算時間。在增加顯示神經影像時只需要增加一點點的記憶體空間。最重要的是Kaleido在視覺化時能夠以最佳的色彩對比套色在鄰近的神經元，讓這些鄰近的神經元可以輕鬆地被辨識出來。以上所提到的工具全部都已經整合在FlyCircuit之中，使用者可以藉由FlyCircuit在線上測試他們的資料。

There were massive neuron images were generated for Connectomics and need to be standardized with automatically tools for further application. To decipher connectome of Drosophila, we have imaged 28,573 single neuron images and 10,000 driver expression images. In the beginning, we have designed an automatic algorithm, NeuroRetriever, for unbiased large-scale segmentation of single neurons from confocal fluorescence images. Using a high-dynamic-range thresholding method to segment single neurons based on branch-specific structural features, by pre-processing the raw images under a wide range of intensity thresholds. Using NeuroRetriever, we successfully retrieved 28,125 single-neuron images from 22,037 raw brain images validated by human segmentation. Next, we have built a database, named FlyDriver, to compare more than 100 million image pairs, i.e., neuron-to-driver and driver-to-driver image pairs. We illustrated the utility of these neuron-driver image-matching data for identifying the putative driver expressed in the target neuron and finding some specific driver covering a set of neuron images. For some particular neuronal ensemble that cannot be specifically represented in one single driver, we used the associated image information among drivers to predict possible intersection or union driver pairs for multiple gene expressions. Finally, we proposed an algorithm named "Kaleido" that can visualize up to at least ten thousand single neurons from the Drosophila brain using only a fraction of the memory traditionally required, without increasing computing time. Adding more brain neurons increases memory only nominally. Importantly, Kaleido maximizes color contrast between neighboring neurons so that individual neurons can be easily distinguished. All of these tools were integrated into FlyCircuit as a plug-in function. Users could test their own data on-line with FlyCircuit.

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