We proposed a novel KBP model using an attention-gating mechanism and on a 3D U-Net architecture for voxel-wise dose prediction of head and neck radiotherapy plans.
Fig. 3. The CT image, KBP predicted dose distribution, ground-truth dose distribution, and voxel-wise dose difference map (predicted – ground-truth) in the axial, sagittal, and coronal planes of a sample head and neck patient plan in the test set.
Fig. 4. The predicted dose distributions were presented side-by-side with corresponding ground-truth dose distributions in addition to difference maps for eight patients in the test set.
The datasets can be found in the OpenKBP - 2020 AAPM Grand Challenge repository at https://competitions.codalab.org/competitions/23428.
Please cite this paper: Osman AFI, Tamam NM. Attention-aware 3D U-Net convolutional neural network for knowledge‐based planning 3D dose distribution prediction of head and neck cancer. Appl Clin Med Phys. 2022;e13630. https://pubmed.ncbi.nlm.nih.gov/35533234/