Wernitznig Stefan, Sele Mariella, Urschler Martin, Zankel Armin, Pölt Peter, Rind F Claire, Leitinger Gerd
Institute of Cell Biology, Histology and Embryology, Research Unit Electron Microscopic Techniques, Medical University of Graz, Harrachgasse 21, 8010 Graz, Austria.
Ludwig Boltzmann Institute for Clinical Forensic Imaging, Universitätsplatz 4, 8010 Graz, Austria; Institute for Computer Graphics and Vision, BioTechMed-Graz, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Austria.
J Neurosci Methods. 2016 May 1;264:16-24. doi: 10.1016/j.jneumeth.2016.02.019. Epub 2016 Feb 27.
Elucidating the anatomy of neuronal circuits and localizing the synaptic connections between neurons, can give us important insights in how the neuronal circuits work. We are using serial block-face scanning electron microscopy (SBEM) to investigate the anatomy of a collision detection circuit including the Lobula Giant Movement Detector (LGMD) neuron in the locust, Locusta migratoria. For this, thousands of serial electron micrographs are produced that allow us to trace the neuronal branching pattern.
The reconstruction of neurons was previously done manually by drawing cell outlines of each cell in each image separately. This approach was very time consuming and troublesome. To make the process more efficient a new interactive software was developed. It uses the contrast between the neuron under investigation and its surrounding for semi-automatic segmentation.
For segmentation the user sets starting regions manually and the algorithm automatically selects a volume within the neuron until the edges corresponding to the neuronal outline are reached. Internally the algorithm optimizes a 3D active contour segmentation model formulated as a cost function taking the SEM image edges into account. This reduced the reconstruction time, while staying close to the manual reference segmentation result.
Our algorithm is easy to use for a fast segmentation process, unlike previous methods it does not require image training nor an extended computing capacity.
Our semi-automatic segmentation algorithm led to a dramatic reduction in processing time for the 3D-reconstruction of identified neurons.
阐明神经回路的解剖结构并定位神经元之间的突触连接，能让我们深入了解神经回路的工作方式。我们正在使用连续块面扫描电子显微镜（SBEM）来研究一种碰撞检测回路的解剖结构，该回路包括蝗虫（飞蝗）中的小叶巨型运动探测器（LGMD）神经元。为此，我们制作了数千张连续电子显微照片，以便追踪神经元的分支模式。
以前，神经元的重建是通过分别手动绘制每张图像中每个细胞的轮廓来完成的。这种方法非常耗时且麻烦。为了提高效率，我们开发了一种新的交互式软件。它利用被研究神经元与其周围环境之间的对比度进行半自动分割。
对于分割，用户手动设置起始区域，算法会自动在神经元内选择一个体积，直到到达与神经元轮廓对应的边缘。在内部，该算法优化了一个三维活动轮廓分割模型，该模型被制定为一个考虑到扫描电子显微镜图像边缘的代价函数。这减少了重建时间，同时与手动参考分割结果相近。
与之前的方法不同，我们的算法易于使用，可实现快速分割过程，它不需要图像训练，也不需要强大的计算能力。
我们的半自动分割算法显著减少了已识别神经元三维重建的处理时间。
