图 书 馆
利用稀疏层析成像重建提高神经回路电子显微镜的深度分辨率Increasing Depth Resolution of Electron Microscopy of Neural Circuits using Sparse Tomographic Reconstruction |
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| 课程网址: | http://videolectures.net/cvpr2010_chklovskii_idre/ |
| 主讲教师: | Dmitri Chklovskii |
| 开课单位: | 霍华德·休斯医学研究所 |
| 开课时间: | 2010-07-19 |
| 课程语种: | 英语 |
| 中文简介: |  神经科学的未来进展取决于神经元回路到单个突触水平的重建。由于神经元结构的特殊性，必须以非常高的分辨率和吞吐量进行成像。虽然电子显微镜（EM）在横向上达到所需的分辨率，但其深度分辨率是严重的限制。计算机断层扫描（CT）可以与电子显微镜结合使用以改善深度分辨率，但是这严重限制了吞吐量，因为需要获取数十或数百个EM图像。在这里，我们利用信号处理的最新进展来计算得到高深度分辨率的EM图像。首先，我们表明，脑组织可以表示为局部基函数的稀疏线性组合，这些基函数是在各个方向上定向的薄膜状结构。然后，我们开发了受压缩感知启发的重建技术，可以从每个部分的极少数（通常是5个）断层摄影视图重建脑组织。这样可以追踪跨层的神经元连接，从而实现神经回路的高通量重建，达到单个突触的水平。 |
| 课程简介: | Future progress in neuroscience hinges on reconstruction of neuronal circuits to the level of individual synapses. Because of the specifics of neuronal architecture, imaging must be done with very high resolution and throughput. While Electron Microscopy (EM) achieves the required resolution in the transverse directions, its depth resolution is a severe limitation. Computed tomography (CT) may be used in conjunction with electron microscopy to improve the depth resolution, but this severely limits the throughput since several tens or hundreds of EM images need to be acquired. Here, we exploit recent advances in signal processing to obtain high depth resolution EM images computationally. First, we show that the brain tissue can be represented as sparse linear combination of local basis functions that are thin membrane-like structures oriented in various directions. We then develop reconstruction techniques inspired by compressive sensing that can reconstruct the brain tissue from very few (typically 5) tomographic views of each section. This enables tracing of neuronal connections across layers and, hence, high throughput reconstruction of neural circuits to the level of individual synapses. |
| 关 键 词: | 神经科学; 神经元回路; 信号处理 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-03-12：lxf |
| 阅读次数: | 29 |