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To improve the inter-slice resolution of the orbital bones including cortical and thin bones, we propose an Orbital Bone- Super Resolution (OB-SR) with combined sagittal and axial MAE loss and sagittal Thin-Bone Structure Aware (TSA) loss. Our method consists of three stages: data preprocessing, intermediate slices generation in the sagittal plane and the orbital bone quality improvement in the axial plane. In the intermediate slices generation stage, a 2D CNN consisting of 6 convolutional layers for feature extraction, an up-sampling layer, and 4 convolutional layers for high-frequency detail refinement is performed. The loss is calculated as the sum of the two mean absolute error (MAE) losses to improve the quality of the output image from the up-sampling layer and the last convolutional layer. In the orbital bone quality improvement stage, the generated intermediate slices are compared to the corresponding original axial images using the axial MAE loss. Experimental results showed that our method can generate the intermediate slices with clear orbital bones in the sagittal and axial images and enhances the boundaries of the thin bone.

Sagittal craniosynostosis is the most common craniosynostosis and the premature closure of the sagittal suture makes the skull shape biparietal narrow and elongated. Surgery in infants with sagittal craniosynostosis is a common treatment for correcting the shape of the deformed skull, but the evaluation of the morphological improvement of the skull before and after operation is made by the surgeon’s subjective judgment. Therefore, we propose an efficient method to quantify the shape of the skull before and after surgery based on the mean normal skull model to assess the surgical outcome. In the preprocessing step, to construct the skull model from the pre- and post-operative CT images, each skull model is constructed consisting of the outer surface of the skull. To distinguish individual cranial bones separated by sutures, mean normal skull model is composed of five cranial bones. In the skull model deformation step, to distinguish from the whole bone of the preoperative skull model to the regional bone, the mean normal skull model is deformed into a preoperative skull model, and the deformed mean normal skull model is again deformed into a postoperative skull model. In the regional skull shape index calculation step, to evaluate the degree of expansion and reduction of postoperative skull relative to the preoperative skull, the regional skull shape index is calculated. Experimental results showed that our regional skull shape index can quantify the degree of expansion and reduction of the postoperative skull relative to the preoperative skull of each cranial bone.

We propose an automatic segmentation of the orbital bone in 3D maxillofacial CT image with double-bone-segmentation network for reconstruction of the orbital bone. Due to similar intensity value with surrounding tissues and low intensity value in thin bone, there is a limitation of under-segmentation in thin bone. To improve segmentation of thin bone, we divide into cortical and thin bone and apply to single-bone-segmentation network respectively. Experimental results show that our DBS-Net results in the improved segmentation of the orbital bone, especially in thin bone of orbital medial wall and the orbital floor.

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.

This paper proposes morphological descriptors representing the degree of skull deformity for craniosynostosis in head CT images and a hierarchical classifier model distinguishing among normal and different types of craniosynostosis. First, to compare deformity surface model with mean normal surface model, mean normal surface models are generated for each age range and the mean normal surface model is deformed to the deformity surface model via multi-level threestage registration. Second, four shape features including local distance and area ratio indices are extracted in each five cranial bone. Finally, hierarchical SVM classifier is proposed to distinguish between the normal and deformity. As a result, the proposed method showed improved classification results compared to traditional cranial index. Our method can be used for the early diagnosis, surgical planning and postsurgical assessment of craniosynostosis as well as quantitative analysis of skull deformity.

We propose an automatic vessel segmentation method of vertebral arteries in CT angiography using combined circular and cylindrical model fitting. First, to generate multi-segmented volumes, whole volume is automatically divided into four segments by anatomical properties of bone structures along z-axis of head and neck. To define an optimal volume circumscribing vertebral arteries, anterior-posterior bounding and side boundaries are defined as initial extracted vessel region. Second, the initial vessel candidates are tracked using circular model fitting. Since boundaries of the vertebral arteries are ambiguous in case the arteries pass through the transverse foramen in the cervical vertebra, the circle model is extended along z-axis to cylinder model for considering additional vessel information of neighboring slices. Finally, the boundaries of the vertebral arteries are detected using graph-cut optimization. From the experiments, the proposed method provides accurate results without bone artifacts and eroded vessels in the cervical vertebra.

Computed tomography angiography (CTA) is currently considered noninvasive potential alternative to conventional digital subtraction angiography (DSA) for the evaluation of lower extremity arteries. For the diagnosis of peripheral arterial occlusive disease, lower extremity vessels in CTA images are extracted in advance. We propose an automatic vessel extraction method using multi-segmented volume and regional vessel tracking in lower extremity CT angiography. To consider an anatomical characteristic of each lower extremity vessel structure, whole volume is automatically divided into five segments such as foot, tibia, knee, femur and pelvis along z-axis of the lower extremities. The vessels and bones are extracted by three-dimensional region growing with multi-seeding and iterative multiple threshold estimation. Finally, to restore the eroded vessels near to bones and cavernous vessels in pelvis and tibia, regional vessel tracking considering density, size and direction is performed. Experimental results show that our method provides accurate results in occluded and stenosed vessels without loss of soft tissue and calcification. For visual scoring, two radiologists compared paired images obtained from proposed method and conventional angiography.