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Semi-supervised Segmentation Using Multiple Segmentation Hypotheses from a Single Atlas

  • Tobias Gass
  • Gábor Székely
  • Orcun Goksel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7766)

Abstract

A semi-supervised segmentation method using a single atlas is presented in this paper. Traditional atlas-based segmentation suffers from either a strong bias towards the selected atlas or the need for manual effort to create multiple atlas images. Similar to semi-supervised learning in computer vision, we study a method which exploits information contained in a set of unlabelled images by mutually registering them non-rigidly and propagating the single atlas segmentation over multiple such registration paths to each target. These multiple segmentation hypotheses are then fused by local weighting based on registration similarity. Our results on two datasets of different anatomies and image modalities, corpus callosum MR and mandible CT images, show a significant improvement in segmentation accuracy compared to traditional single atlas based segmentation. We also show that the bias towards the selected atlas is minimized using our method. Additionally, we devise a method for the selection of intermediate targets used for propagation, in order to reduce the number of necessary inter-target registrations without loss of final segmentation accuracy.

Keywords

Target Image Label Propagation Segmentation Accuracy Normalize Cross Correlation Support Sample 
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-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Tobias Gass
    • 1
  • Gábor Székely
    • 1
  • Orcun Goksel
    • 1
  1. 1.Computer Vision Lab, Dep. of Electrical EngineeringETH ZurichSwitzerland

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