attention-aware-3D-UNet-for-RT-dose-prediction

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attention-aware-3D-UNet-for-RT-dose-prediction

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

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# 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.

![image](https://user-images.githubusercontent.com/10604649/236342327-fe4fd251-e336-494e-a9f2-6e90c47fbdcc.png)

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.

![image](https://user-images.githubusercontent.com/10604649/236342385-98de63bf-17b5-4c1f-8606-29ae00d3c3b5.png)

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/

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