Segmentation of the orbital bone is necessary for orbital wall reconstruction in cranio-maxillofacial surgery to supportthe eyeball position and restore the volume and shape of the orbit. However, orbital bone segmentation has a challengingissue that the orbital bone is composed of high-intensity cortical bones and low-intensity trabecular and thin bones.Especially, the thin bones of the orbital medial wall and the orbital floor have similar intensity values that areindistinguishable from surrounding soft tissues due to the partial volume effect that occurs when CT images aregenerated. Thus, we propose an orbital bone segmentation method using multi-graylevel FCNs that segment corticalbone, trabecular bone and thin bones with different intensities in head-and-neck CT images. To adjust the imageproperties of each dataset, pixel spacing normalization and the intensity normalization is performed. To overcome theunder-segmentation of the thin bones of the orbital medial wall, a single orbital bone mask is divided into cortical andthin bone masks. Multi-graylevel FCNs are separately trained on the cortical and thin bone masks based on 2D U-Net,and each cortical and thin bone segmentation result is integrated to obtain the whole orbital bone segmentation result. Asa result, it showed that multi-graylevel FCNs improves segmentation accuracy of the thin bones of the medial wallcompared to a single gray-level FCNs and thresholding.
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