Automatic Construction of 3D Statistical Deformation Models Using Non-rigid Registration
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
Semi-Supervised Learning
Diffeomorphic registration using b-splines
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Multi-atlas segmentation with robust label transfer and label fusion
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
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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.