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.
We generate super-resolution orbital bone CT images using 3D SRGAN network to improve the accuracy of segmentation of thin bones with large slice thickness. Our method consists of data preparation and super-resolution thin bone image generation. Experimental results show that the generated super-resolution images using 3D SRGAN are clearly observed in thin bone areas and are more similar to the original high resolution images than other images using bilinear and bicubic interpolations.
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