2011
DOI: 10.1007/978-3-642-23623-5_77
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Multiple Structure Tracing in 3D Electron Micrographs

Abstract: Abstract. 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 smoothn… Show more

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Cited by 6 publications
(7 citation statements)
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“…There has been a lot of interest in Electron Micrograph segmentation and tracing [5], [10], [23]- [26]. The primary motivation behind the proposed technique is the 3D tracing problem in retinal connectome data [27], a problem (data) not solved by any of the above referred techniques.…”
Section: A Experimental Results On Electron Micrograph Tracingmentioning
confidence: 99%
See 1 more Smart Citation
“…There has been a lot of interest in Electron Micrograph segmentation and tracing [5], [10], [23]- [26]. The primary motivation behind the proposed technique is the 3D tracing problem in retinal connectome data [27], a problem (data) not solved by any of the above referred techniques.…”
Section: A Experimental Results On Electron Micrograph Tracingmentioning
confidence: 99%
“…There exists substantial prior work in segmentation based tracing, with frameworks ranging from shortest path based [2], watersheds [3], [4], random walkers [5], active contours based snakes [6], geodesic active contours, vector flows [7], active contours without edges [8], and discrete valued Markov Random Fields (MRFs) inferred using graph cuts [9], [10]. The aim of this paper is to present a method that is capable of wrapping around a non-parametric segmentation technique, thereby achieving two objectives: Firstly, the proposed technique is capable of embedding high level priors into the tracing algorithm using free parameters of the base segmenter.…”
Section: Background and Related Workmentioning
confidence: 99%
“…Figure 3(a) illustrates the result of tracing all structures over the first few frames of the dataset. In order to place the proposed algorithm in context with existing state of the art techniques, we compare performance with Graph Cuts based P n model [6], Level Sets using the Chan-Vese model, and bi-graph diffusion, see Figure 3. The striking aspect of experiments is the running time of algorithms.…”
Section: Methodsmentioning
confidence: 99%
“…The works by [7,8,6,12] are good sources of reference for EM image analysis. Further, [5] utilized hypergraphs for unsupervised video segmentation, in contrast to the supervised case the proposed approach deals with.…”
Section: Related Workmentioning
confidence: 99%
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