%Aigaion2 BibTeX export from HES SO Valais Publications
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@INPROCEEDINGS{,
     author = {Joyseeree, Ranveer and Jimenez del Toro, Oscar and M{\"{u}}ller, Henning},
      title = {Using Probability Maps for Multi-organ Automatic Segmentation},
  booktitle = {MICCAI MCV workshop},
       year = {2014},
   location = {Nagoya, Japan},
   abstract = {Organ segmentation is a vital task in diagnostic medicine. The ability to perform it automatically can save clinicians time and labor. In this paper, a method to achieve automatic segmentation of organs in three{dimensional (3D), non{annotated, full{body magnetic resonance (MR), and computed tomography (CT) volumes is proposed. According to the method, training volumes are registered to a chosen reference volume and the registration transform obtained is used to create an overlap volume for each annotated organ in the dataset. A 3D probability map, and its centroid, is derived from that. Afterwards, the reference volume is anely mapped onto any non{annotated volume and the obtained mapping is applied to the centroid and the organ probability maps.
Region{growing segmentation on the non{annotated volume may then be started using the warped centroid as the seed point and the warped
probability map as an aid to the stopping criterion.}
}