Abstract. This paper proposes an approach to improve atlas-to-image registration accuracy with large pathologies. Instead of directly registering an atlas to a pathological image, the method learns a mapping from the pathological image to a quasi-normal image, for which more accurate registration is possible. Specifically, the method uses a deep variational convolutional encoder-decoder network to learn the mapping. Furthermore, the method estimates local mapping uncertainty through network inference statistics and uses those estimates to down-weight the image registration similarity measure in areas of high uncertainty. The performance of the method is quantified using synthetic brain tumor images and images from the brain tumor segmentation challenge (BRATS 2015).
REF used a Variational Auto-encoder to learn a mapping from pathological images to quasi-normal (pseudo healthy) images to improve atlas-to-image registration accuracy with large pathologies.
Registration of pathological images
Simulation and synthesis in medical imaging : first International Workshop, SASHIMI 2016, held in conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings. SASHIMI (Workshop)
Simulation and synthesis in medical imaging : first International Workshop, SASHIMI 2016, held in conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings. SASHIMI (Workshop)
Computer Science Medicine