Axon tracking in serial block-face scanning electron microscopy

Jurrus, Elizabeth; Hardy, Melissa; Taşdizen, Tolga; Fletcher, P. Thomas; Koshevoy, P.; Chien, Chi-­Bin; Denk, Winfried; Whitaker, Ross T.

doi:10.1016/j.media.2008.05.002

Cited by 94 publications

(76 citation statements)

References 29 publications

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“…In high-magnification 3D images of neurites running largely in the axial direction, region-growing methods may be used to segment the neurites in one optical section, whose centroid positions can serve as seed points to initiate segmentation in the next section (66,67), reminiscent of mean-shift tracking (68,69) and activecontour based propagation approaches (70)(71)(72). More robustness can be expected from algorithms that constrain the search to given start and end points, by defining a cost or ''energy'' function that assigns a penalty to connecting any two neighboring points (computed from local image features at these points), and minimizing the cumulative cost from start to end point (27,58,(73)(74)(75)(76).…”

Section: Tree Segmentationmentioning

confidence: 99%

Neuron tracing in perspective

2010

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The study of the structure and function of neuronal cells and networks is of crucial importance in the endeavor to understand how the brain works. A key component in this process is the extraction of neuronal morphology from microscopic imaging data. In the past four decades, many computational methods and tools have been developed for digital reconstruction of neurons from images, with limited success. As witnessed by the growing body of literature on the subject, as well as the organization of challenging competitions in the field, the quest for a robust and fully automated system of more general applicability still continues. The aim of this work, is to contribute by surveying recent developments in the field for anyone interested in taking up the challenge. Relevant aspects discussed in the article include proposed image segmentation methods, quantitative measures of neuronal morphology, currently available software tools for various related purposes, and morphology databases. ' 2010 International Society for Advancement of Cytometry

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Section: Tree Segmentationmentioning

confidence: 99%

Neuron tracing in perspective

2010

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“…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%

Scalable Tracing of Electron Micrographs by Fusing Top Down and Bottom Up Cues Using Hypergraph Diffusion

Jagadeesh

Shih

Manjunath

et al. 2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

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Abstract.A novel framework for robust 3D tracing in Electron Micrographs is presented. The proposed framework is built using ideas from hypergraph diffusion, and achieves two main objectives. Firstly, the approach scales to trace hundreds of targets without noticeable increase in runtime complexity. Secondly, the framework yields flexibility to fuse top down (global cues as hyperedges) and bottom up (local superpixels as nodes) information. Subsequently, a procedure for auto-seeding to initialize the tracing procedure is proposed. The paper concludes with experimental validation on a challenging large scale tracing problem for simultaneously tracing 95 structures, illustrating applicability of the proposed algorithm.

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“…Therefore, researchers have put in a great amount of effort in developing segmentation algorithms to extract neuronal morphologies from stacks of serial EM images [1,2,3,4,5,6]. A great majority of these existing segmentation algorithms are designed for the automation of the segmentation pipeline.…”

Section: Introductionmentioning

confidence: 99%

An Interactive Editing Framework for Electron Microscopy Image Segmentation

Yang

Choe

2011

Advances in Visual Computing

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Abstract. There are various automated segmentation algorithms for medical images. However, 100% accuracy may be hard to achieve because medical images usually have low contrast and high noise content. These segmentation errors may require manual correction. In this paper, we present an interactive editing framework that allows the user to quickly correct segmentation errors produced by automated segmentation algorithms. The framework includes two editing methods: (1) editing through multiple choice and (2) interactive editing through graph cuts. The first method provides a set of alternative segmentations generated from a confidence map that carries valuable information from multiple cues, such as the probability of a boundary, an intervening contour cue, and a soft segmentation by a random walker. The user can then choose the most acceptable one from those segmentation alternatives. The second method introduces an interactive editing tool where the user can interactively connect or disconnect presegmented regions. The editing task is posed as an energy minimization problem: We incorporate a set of constraints into the energy function and obtain an optimal solution via graph cuts. The results show that the proposed editing framework provides a promising solution for the efficient correction of incorrect segmentation results.

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Axon tracking in serial block-face scanning electron microscopy

Cited by 94 publications

References 29 publications

Neuron tracing in perspective

Neuron tracing in perspective

Scalable Tracing of Electron Micrographs by Fusing Top Down and Bottom Up Cues Using Hypergraph Diffusion

An Interactive Editing Framework for Electron Microscopy Image Segmentation

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