Orbital bone segmentation in head and neck CT images using multi-gray level fully convolutional networks

Abstract

Segmentation of the orbital bone is necessary for orbital wall reconstruction in cranio-maxillofacial surgery to support the eyeball position and restore the volume and shape of the orbit. However, orbital bone segmentation has a challenging issue 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 are indistinguishable from surrounding soft tissues due to the partial volume effect that occurs when CT images are generated. Thus, we propose an orbital bone segmentation method using multi-graylevel FCNs that segment cortical bone, trabecular bone and thin bones with different intensities in head-and-neck CT images. To adjust the image properties of each dataset, pixel spacing normalization and the intensity normalization is performed. To overcome the under-segmentation of the thin bones of the orbital medial wall, a single orbital bone mask is divided into cortical and thin 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. As a result, it showed that multi-graylevel FCNs improves segmentation accuracy of the thin bones of the medial wall compared to a single gray-level FCNs and thresholding.