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Using Probability Maps for Multi-organ Automatic Segmentation
Type of publication: Inproceedings
Citation:
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.
Keywords:
Authors Joyseeree, Ranveer
Jimenez del Toro, Oscar
Müller, Henning
Added by: []
Total mark: 0
Attachments
  • MICCAI_Workshop_2013_Ranveer.p...
Notes
  • []: IF 2005=0.402
Topics