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Refining Mitochondria Segmentation in Electron Microscopy Imagery with Active Surfaces

  • Anne JorstadEmail author
  • Pascal Fua
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8928)

Abstract

We present an active surface-based method for refining the boundary surfaces of mitochondria segmentation data. We exploit thefact that mitochondria have thick dark membranes, so referencing the image data at the inner membrane can help drive a more accurate delineation of the outer membrane surface. Given the initial boundary prediction from a machine learning-based segmentation algorithm as input, we compare several cost functions used to drive an explicit update scheme to locally refine 3D mesh surfaces, and results are presented on electron microscopy imagery. Our resulting surfaces are seen to fit very accurately to the mitochondria membranes, more accurately even than the available hand-annotations of the data.

Keywords

Energy Function Image Gradient Initial Segmentation Mitochondrion Membrane Laplacian Smoothing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Ecole Polytechnique Fédérale de LausanneLausanneSwitzerland

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