Conference paper
Cochlea Segmentation using Iterated Random Walks with Shape Prior
Pompeu Fabra University1
Department of Applied Mathematics and Computer Science, Technical University of Denmark2
Visual Computing, Department of Applied Mathematics and Computer Science, Technical University of Denmark3
Scientific Computing, Department of Applied Mathematics and Computer Science, Technical University of Denmark4
Alma Medical Systems5
Cochlear implants can restore hearing to deaf or partially deaf patients. In order to plan the intervention, a model from high resolution μCT images is to be built from accurate cochlea segmentations and then, adapted to a patient-specific model. Thus, a precise segmentation is required to build such a model.
We propose a new framework for segmentation of μCT cochlear images using random walks where a region term is combined with a distance shape prior weighted by a confidence map to adjust its influence according to the strength of the image contour. Then, the region term can take advantage of the high contrast between the background and foreground and the distance prior guides the segmentation to the exterior of the cochlea as well as to less contrasted regions inside the cochlea.
Finally, a refinement is performed preserving the topology using a topological method and an error control map to prevent boundary leakage. We tested the proposed approach with 10 datasets and compared it with the latest techniques with random walks and priors. The experiments suggest that this method gives promising results for cochlea segmentation.
Language: | English |
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Publisher: | SPIE - International Society for Optical Engineering |
Year: | 2016 |
Pages: | 97842U-97842U-9 |
Proceedings: | SPIE Medical Imaging 2016 |
Series: | Proceedings of Spie - the International Society for Optical Engineering |
ISBN: | 1510600191 and 9781510600195 |
ISSN: | 1996756x , 0277786x , 24109045 and 16057422 |
Types: | Conference paper |
DOI: | 10.1117/12.2208675 |
ORCIDs: | Kjer, Hans Martin |