Jagadeesh Vignesh, Vu Nhat, Manjunath B S
Department of ECE and Center for Bioimage Informatics, University of California, Santa Barbara, CA 93106, USA.
Med Image Comput Comput Assist Interv. 2011;14(Pt 1):613-20. doi: 10.1007/978-3-642-23623-5_77.
Automatic interpretation of Transmission Electron Micrograph (TEM) volumes is central to advancing current understanding of neural circuitry. In the context of TEM image analysis, tracing 3D neuronal structures is a significant problem. This work proposes a new model using the conditional random field (CRF) framework with higher order potentials for tracing multiple neuronal structures in 3D. The model consists of two key features. First, the higher order CRF cost is designed to enforce label smoothness in 3D and capture rich textures inherent in the data. Second, a technique based on semi-supervised edge learning is used to propagate high confidence structural edges during the tracing process. In contrast to predominantly edge based methods in the TEM tracing literature, this work simultaneously combines regional texture and learnt edge features into a single framework. Experimental results show that the proposed method outperforms more traditional models in tracing neuronal structures from TEM stacks.
透射电子显微镜（TEM）图像的自动解读对于深化当前对神经回路的理解至关重要。在TEM图像分析的背景下，追踪三维神经元结构是一个重大问题。这项工作提出了一种新模型，该模型使用具有高阶势的条件随机场（CRF）框架来追踪三维中的多个神经元结构。该模型包含两个关键特征。第一，高阶CRF代价被设计用于在三维中强制标签平滑，并捕捉数据中固有的丰富纹理。第二，一种基于半监督边缘学习的技术被用于在追踪过程中传播高置信度的结构边缘。与TEM追踪文献中主要基于边缘的方法不同，这项工作将区域纹理和学习到的边缘特征同时整合到一个单一框架中。实验结果表明，所提出的方法在从TEM堆栈追踪神经元结构方面优于更传统的模型。
