Similarity based object retrieval of composite neuronal structures

  • Authors:

  • F. Schulze;M. Trapp;K. Bühler;T. Liu;B. Dickson

  • Affiliations:

  • VRVis Forschungs GmbH, Austria;VRVis Forschungs GmbH, Austria;VRVis Forschungs GmbH, Austria;Institute of Molecular Pathology, Austria;Institute of Molecular Pathology, Austria

  • Venue:
  • EG 3DOR'12 Proceedings of the 5th Eurographics conference on 3D Object Retrieval
  • Year:
  • 2012

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Abstract

Circuit Neuroscience tries to solve one of the most challenging questions in biology: How does the brain work? An important step towards an answer to this question is to gather detailed knowledge about the neuronal circuits of the model organism Drosophila melanogaster. Geometric representations of neuronal objects of the Drosophila are acquired using molecular genetic methods, confocal microscopy, non-rigid registration and segmentation. These objects are integrated into a constantly growing common atlas. The comparison of new segmented neurons to already known neurons is a frequent task which evolves with a growing amount of data into a bottleneck of the knowledge discovery process. Thus, the exploration of the atlas by means of domain specific similarity measures becomes a pressing need. To enable similarity based retrieval of neuronal objects we defined together with domain experts tailored dissimilarity measures for each of the three typical neuronal sub structures cell body, projection, arborization. The dissimilarity measure for composite neurons has been defined as domain specific combination of the sub structure dissimilarities. According to domain experts the developed system has big advantages for all tasks which involve extensive data exploration.