Improvement of inter-slice resolution based on 2D CNN with thin bone structure-aware on head-and-neck CT images

Hee Rim Yun; Min Jin Lee; Helen Hong; Kyu Won Shim; Jonghong Jeon

doi:10.1117/12.2582342

15 February 2021 Improvement of inter-slice resolution based on 2D CNN with thin bone structure-aware on head-and-neck CT images

Hee Rim Yun, Min Jin Lee, Helen Hong, Kyu Won Shim, Jonghong Jeon

Author Affiliations +

Proceedings Volume 11596, Medical Imaging 2021: Image Processing; 1159627 (2021) https://doi.org/10.1117/12.2582342
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

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.

Conference Presentation

Citation Download Citation

Hee Rim Yun, Min Jin Lee, Helen Hong, Kyu Won Shim, and Jonghong Jeon "Improvement of inter-slice resolution based on 2D CNN with thin bone structure-aware on head-and-neck CT images", Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 1159627 (15 February 2021); https://doi.org/10.1117/12.2582342

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