Becker Carlos, Ali Karim, Knott Graham, Fua Pascal
Computer Vision Lab, Ecole Polytechnique Fédérale de Lausanne, Switzerland.
Med Image Comput Comput Assist Interv. 2012;15(Pt 1):585-92. doi: 10.1007/978-3-642-33415-3_72.
We present a new approach for the automated segmentation of excitatory synapses in image stacks acquired by electron microscopy. We rely on a large set of image features specifically designed to take spatial context into account and train a classifier that can effectively utilize cues such as the presence of a nearby post-synaptic region. As a result, our algorithm successfully distinguishes synapses from the numerous other organelles that appear within an EM volume, including those whose local textural properties are relatively similar. This enables us to achieve very high detection rates with very few false positives.
我们提出了一种新方法，用于对通过电子显微镜获取的图像堆栈中的兴奋性突触进行自动分割。我们依赖于大量专门设计用于考虑空间上下文的图像特征，并训练一个能够有效利用诸如附近突触后区域存在等线索的分类器。结果，我们的算法成功地将突触与出现在电子显微镜体积内的众多其他细胞器区分开来，包括那些局部纹理属性相对相似的细胞器。这使我们能够以极少的误报实现非常高的检测率。
