Semi-Supervised Learning of Anatomical Manifolds for Atlas-Based Segmentation of Medical Images
2016 (English)In: Proceedings of the 23rd International Conference on Pattern Recognition (ICPR), IEEE Computer Society, 2016Conference paper, Poster (Refereed)
This paper presents a novel method for atlas-based segmentation of medical images. The method uses semi- supervised learning of a graph describing a manifold of anatom- ical variations of whole-body images, where unlabelled data are used to find a path with small deformations from the labelled atlas to the target image. The method is evaluated on 36 whole-body magnetic resonance images with manually segmented livers as ground truth. Significant improvement (p < 0.001) was obtained compared to direct atlas-based registration.
Place, publisher, year, edition, pages
IEEE Computer Society, 2016.
MRI, atlas-based segmentation
Medical Image Processing
IdentifiersURN: urn:nbn:se:liu:diva-136004OAI: oai:DiVA.org:liu-136004DiVA: diva2:1084329
International Conference on Pattern Recognition (ICPR)