Howard Hughes Medical Institute and Brain & Cog. Sci. Dept., Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Curr Opin Neurobiol. 2010 Oct;20(5):653-66. doi: 10.1016/j.conb.2010.07.004.
Connections between neurons can be found by checking whether synapses exist at points of contact, which in turn are determined by neural shapes. Finding these shapes is a special case of image segmentation, which is laborious for humans and would ideally be performed by computers. New metrics properly quantify the performance of a computer algorithm using its disagreement with 'true' segmentations of example images. New machine learning methods search for segmentation algorithms that minimize such metrics. These advances have reduced computer errors dramatically. It should now be faster for a human to correct the remaining errors than to segment an image manually. Further reductions in human effort are expected, and crucial for finding connectomes more complex than that of Caenorhabditis elegans.
神经元之间的连接可以通过检查接触点是否存在突触来发现，而突触的存在又取决于神经元的形状。找到这些形状是图像分割的一个特例，对人类来说很费力，理想情况下应由计算机来完成。新的度量标准可以通过比较其与示例图像“真实”分割的差异，正确地量化计算机算法的性能。新的机器学习方法搜索能够最小化这些度量标准的分割算法。这些进展大大降低了计算机的错误率。现在，人类纠正剩余错误的速度应该比手动分割图像更快。预计人类的工作量还会进一步减少，这对于发现比秀丽隐杆线虫更复杂的连接组至关重要。
