Deep models for brain EM image segmentation: novel insights and improved performance.

Journal: Bioinformatics (Oxford, England)
Published Date: Mar 25, 2016

Abstract

MOTIVATION: Accurate segmentation of brain electron microscopy (EM) images is a critical step in dense circuit reconstruction. Although deep neural networks (DNNs) have been widely used in a number of applications in computer vision, most of these models that proved to be effective on image classification tasks cannot be applied directly to EM image segmentation, due to the different objectives of these tasks. As a result, it is desirable to develop an optimized architecture that uses the full power of DNNs and tailored specifically for EM image segmentation.

Authors

  • Ahmed Fakhry
    Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA.
  • Hanchuan Peng
    New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China.
  • Shuiwang Ji
    Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA. Electronic address: sji@cs.odu.edu.

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