walidaun / attention-aware-3d-unet-for-rt-dose-prediction

Evaluate the feasibility of attention-gated 3D U-Net for knowledge-based planning dose prediction of head-and-neck radiotherapy.

Attention-gated-3D-UNet-for-radiotherpay-dose-prediction

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.

Availability of data and materials

The datasets can be found in the OpenKBP - 2020 AAPM Grand Challenge repository at https://competitions.codalab.org/competitions/23428.

Paper

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/