Modeling brain circuitry over a wide range of scales

If we are ever to unravel the mysteries of brain function at its most fundamental level, we will need a precise understanding of how its component neurons connect to each other. Electron Microscopes (EM) can now provide the nanometer resolution that is needed to image synapses, and therefore connections, while Light Microscopes (LM) see at the micrometer resolution required to model the 3D structure of the dendritic network. Since both the topology and the connection strength are integral parts of the brain's wiring diagram, being able to combine these two modalities is critically important. In fact, these microscopes now routinely produce high-resolution imagery in such large quantities that the bottleneck becomes automated processing and interpretation, which is needed for such data to be exploited to its full potential. In this paper, we briefly review the Computer Vision techniques we have developed at EPFL to address this need. They include delineating dendritic arbors from LM imagery, segmenting organelles from EM, and combining the two into a consistent representation.

[1]  Pascal Fua,et al.  Learning Structured Models for Segmentation of 2-D and 3-D Imagery , 2015, IEEE Transactions on Medical Imaging.

[2]  Pascal Fua,et al.  Reconstructing Loopy Curvilinear Structures Using Integer Programming , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Angela C. Poole,et al.  The PINK1/Parkin pathway regulates mitochondrial morphology , 2008, Proceedings of the National Academy of Sciences.

[4]  Nathan Ratliff,et al.  Online) Subgradient Methods for Structured Prediction , 2007 .

[5]  G. Perkins,et al.  Mitochondrial fragmentation in neurodegeneration , 2008, Nature Reviews Neuroscience.

[6]  Eugene W. Myers,et al.  Automated Reconstruction of Neuronal Morphology Based on Local Geometrical and Global Structural Models , 2011, Neuroinformatics.

[7]  Doyun Lee,et al.  Target Cell-Specific Involvement of Presynaptic Mitochondria in Post-Tetanic Potentiation at Hippocampal Mossy Fiber Synapses , 2007, The Journal of Neuroscience.

[8]  Pascal Fua,et al.  Refining Mitochondria Segmentation in Electron Microscopy Imagery with Active Surfaces , 2014, ECCV Workshops.

[9]  Eugene W. Myers,et al.  Proof-editing is the Bottleneck Of 3D Neuron Reconstruction: The Problem and Solutions , 2011, Neuroinformatics.

[10]  Pascal Fua,et al.  Exploiting Enclosing Membranes and Contextual Cues for Mitochondria Segmentation , 2014, MICCAI.

[11]  Pascal Fua,et al.  Initializing snakes [object delineation] , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Mark W. Schmidt,et al.  Block-Coordinate Frank-Wolfe Optimization for Structural SVMs , 2012, ICML.

[13]  Vivek Mehta,et al.  Automated Tracing of Neurites from Light Microscopy Stacks of Images , 2011, Neuroinformatics.

[14]  Xing Zhang,et al.  Retinal Fundus Image Registration via Vascular Structure Graph Matching , 2010, Int. J. Biomed. Imaging.

[15]  Gábor Székely,et al.  Velcro Surfaces: Fast Initialization of Deformable Models , 1997, Comput. Vis. Image Underst..

[16]  Pushmeet Kohli,et al.  Non-parametric Higher-Order Random Fields for Image Segmentation , 2014, ECCV.

[17]  Badrinath Roysam,et al.  Novel 4-D Open-Curve Active Contour and curve completion approach for automated tree structure extraction , 2011, CVPR 2011.

[18]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[19]  Pascal Fua,et al.  Automated Reconstruction of Dendritic and Axonal Trees by Global Optimization with Geometric Priors , 2011, Neuroinformatics.

[20]  Douglas B. Ehlenberger,et al.  New techniques for imaging, digitization and analysis of three-dimensional neural morphology on multiple scales , 2005, Neuroscience.

[21]  Pascal Fua,et al.  Learning for Structured Prediction Using Approximate Subgradient Descent with Working Sets , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Francesc Moreno-Noguer,et al.  Non-Rigid Graph Registration Using Active Testing Search , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Deniz Erdogmus,et al.  Principal Curves as Skeletons of Tubular Objects , 2011, Neuroinformatics.

[24]  Amelio Vázquez Reina,et al.  Radon-Like features and their application to connectomics , 2010, CVPR Workshops.

[25]  J. Munkres ALGORITHMS FOR THE ASSIGNMENT AND TRANSIORTATION tROBLEMS* , 1957 .

[26]  Thomas Hofmann,et al.  Support vector machine learning for interdependent and structured output spaces , 2004, ICML.

[27]  K. Harris,et al.  Mitochondria detection in electron microscopy images , 2008 .

[28]  Shih-Fu Chang,et al.  Automatic Reconstruction of Neural Morphologies with Multi-Scale Tracking , 2012, Front. Neural Circuits.

[29]  Ross T. Whitaker,et al.  Detection of neuron membranes in electron microscopy images using a serial neural network architecture , 2010, Medical Image Anal..

[30]  Yuri Boykov,et al.  Globally optimal segmentation of multi-region objects , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[31]  Max A. Viergever,et al.  Mutual-information-based registration of medical images: a survey , 2003, IEEE Transactions on Medical Imaging.

[32]  S. Campello,et al.  Mitochondrial shape changes: orchestrating cell pathophysiology , 2010, EMBO reports.

[33]  Cordelia Schmid,et al.  IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2004, Washington, DC, USA, June 27 - July 2, 2004 , 2004, CVPR Workshops.

[34]  Paul Suetens,et al.  Robust matching of 3D lung vessel trees , 2010 .

[35]  Badrinath Roysam,et al.  3-D Image Pre-processing Algorithms for Improved Automated Tracing of Neuronal Arbors , 2011, Neuroinformatics.

[36]  Tolga Tasdizen,et al.  Automatic markup of neural cell membranes using boosted decision stumps , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[37]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[38]  Ullrich Köthe,et al.  Automated segmentation of synapses in 3D EM data , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[39]  Pascal Fua,et al.  Learning Context Cues for Synapse Segmentation , 2013, IEEE Transactions on Medical Imaging.

[40]  Alexander G. Gray,et al.  Automatic joint classification and segmentation of whole cell 3D images , 2009, Pattern Recognit..

[41]  David Mayerich,et al.  NetMets: software for quantifying and visualizing errors in biological network segmentation , 2012, BMC Bioinformatics.

[42]  Pascal Fua,et al.  Semi-Automated Reconstruction of Curvilinear Structures in Noisy 2D images and 3D image stacks , 2013 .

[43]  Andrew McCallum,et al.  SampleRank: Training Factor Graphs with Atomic Gradients , 2011, ICML.

[44]  Tolga Tasdizen,et al.  Image Segmentation with Cascaded Hierarchical Models and Logistic Disjunctive Normal Networks , 2013, 2013 IEEE International Conference on Computer Vision.

[45]  Pascal Fua,et al.  Supervoxel-Based Segmentation of Mitochondria in EM Image Stacks With Learned Shape Features , 2012, IEEE Transactions on Medical Imaging.

[46]  Pascal Fua,et al.  Structured Image Segmentation Using Kernelized Features , 2012, ECCV.

[47]  Pascal Fua,et al.  Automated reconstruction of tree structures using path classifiers and Mixed Integer Programming , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[48]  Martin A. Fischler,et al.  Detection of roads and linear structures in low-resolution aerial imagery using a multisource knowledge integration technique☆ , 1981 .

[49]  Yaonan Wang,et al.  Multiscale Bi-Gaussian Filter for Adjacent Curvilinear Structures Detection With Application to Vasculature Images , 2013, IEEE Transactions on Image Processing.