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Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

Volume 7510 of the series Lecture Notes in Computer Science pp 323-330

Anisotropic ssTEM Image Segmentation Using Dense Correspondence across Sections

  • Dmitry LaptevAffiliated withDepartment of Computer Science, ETH Zurich
  • , Alexander VezhnevetsAffiliated withDepartment of Computer Science, ETH Zurich
  • , Sarvesh DwivediAffiliated withDepartment of Computer Science, ETH Zurich
  • , Joachim M. BuhmannAffiliated withDepartment of Computer Science, ETH Zurich

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Abstract

Connectomics based on high resolution ssTEM imagery requires reconstruction of the neuron geometry from histological slides. We present an approach for the automatic membrane segmentation in anisotropic stacks of electron microscopy brain tissue sections. The ambiguities in neuronal segmentation of a section are resolved by using the context from the neighboring sections. We find the global dense correspondence between the sections by SIFT Flow algorithm, evaluate the features of the corresponding pixels and use them to perform the segmentation. Our method is 3.6 and 6.4% more accurate in two different accuracy metrics than the algorithm with no context from other sections.

Keywords

Membrane Segmentation Anisotropic Data Dense Correspondence SIFT Flow