Random walks with shape prior for cochlea segmentation in ex vivo \mu \hbox {CT}$mCT
PurposeCochlear implantation is a safe and effective surgical procedure to restore hearing in deaf patients. However, the level of restoration achieved may vary due to differences in anatomy, implant type and surgical access. In order to reduce the variability of the surgical outcomes, we previously proposed the use of a high resolution model built from \mu \hbox {CT}$mCT images and then adapted to patient specific clinical CT scans. As the accuracy of the model is dependent on the precision of the original segmentation, it is extremely important to have accurate \mu \hbox {CT}$mCT segmentation algorithms.MethodsWe propose a new framework for cochlea segmentation in ex vivo \mu \hbox {CT}$mCT images using random walks where a distance based shape prior is combined with a region term estimated by a Gaussian mixture model. The prior is also weighted by a confidence map to adjust its influence according to the strength of the image contour. Random walks is performed iteratively, and the prior mask is aligned in every iteration.ResultsWe tested the proposed approach in ten \mu \hbox {CT}$mCT data sets and compared it with other random walks based segmentation techniques such as guided random walks (Eslami et al. in Med Image Anal 17(2):236 253, 2013) and constrained random walks (Li et al. in Advances in image and video technology. Springer, Berlin, pp 215 226, 2012) Our approach demonstrated higher accuracy results due to the probability density model constituted by the region term and shape prior information weighed by a confidence map.ConclusionThe weighted combination of the distance based shape prior with a region term into random walks provides accurate segmentations of the cochlea. The experiments suggest that the proposed approach is robust for cochlea segmentation.
International Journal of Computer Assisted Radiology and Surgery