Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
International Journal of Computer Vision
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Higher Order Potentials for Enforcing Label Consistency
International Journal of Computer Vision
Geometrical consistent 3D tracing of neuronal processes in ssTEM data
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
Context enhanced graphical model for object localization in medical images
MCV'12 Proceedings of the Second international conference on Medical Computer Vision: recognition techniques and applications in medical imaging
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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.