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

Tobias Gass, Gabor Székely, Orcun Goksel
MICCAI Workshop on Medical Computer Vision
Nice, France, October 2012


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 nonrigidly 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.

Link to publisher's page
  author = {Tobias Gass and Gabor Székely and Orcun Goksel},
  title = {Semi-supervised Segmentation Using Multiple Segmentation Hypotheses from a Single Atlas},
  booktitle = {MICCAI Workshop on Medical Computer Vision},
  year = {2012},
  month = {October},
  pages = {29-37},
  volume = {7766},
  editor = {Menze and Bjoern Langs and Georg Lu and Le Montillo and Albert Tu and Zhuowen Criminisi and Antonio},
  series = {Lecture notes in computer science},
  publisher = {Springer},
  keywords = {}