3D semi-automatic segmentation of the cochlea and inner ear

Though interactive direct volume rendering produces meaningful images with high quality, it cannot display separate inner ear labyrinth or cochlea only by adjusting imaging parameters to suppress the surrounding structures. Novel semi-automatic segmentation methods were presented to extract the cochlea and inner ear from spiral CT images. The cochlea was separated from the medical image volume by applying the 3D narrow band level set segmentation algorithm with user interaction introduced to locate the initial contour and adjust the parameters. The inner ear was extracted with a similar semi-automatic segmentation method: manual segmentation was first applied to remove several closely interconnected regions in boundary by viewing image volume slice by slice, then the 3D narrow band level set segmentation algorithm was used to complete fine segmentation on image volume. Generating 3D models of cochlea and inner car structures with such methods not only takes advantage of the combination of 2D images with 3D volume but also saves much time of post-processing. The segmented results were rendered with the Marching Cubes surface rendering method. The correlation of the point on the resultant surface to the three orthogonal sections that intersect at that point on the surface was built to evaluate the segmented object and display the spatial relations of the anatomical structures. The performance of the presented semi-automatic segmentation methods is tested using spiral CT images of the temporal bone.