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license: apache-2.0 |
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# UMambaAdj: Advancing GTV Segmentation for Head and Neck Cancer in MRI-Guided RT with UMamba and nnU-Net ResEnc Planner |
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This repository contains the trained weights and validation results of the proposed methods for T2-weighted MRI head and neck tumor segmentation, including GTVp and GTVn segmentation for the [HNTS-MRG 2024 challenge](https://hntsmrg24.grand-challenge.org/). |
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Preprocessing, postprocessing and model codes can be found at [UMambaAdj Github](https://github.com/Aarhus-RadOnc-AI/UMambaAdj). |
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## Available Model Weights |
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The trained weights and validation results are stored in the following directories: |
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- nnUNetTrainerResenc__nnUNetResEncUNetMPlans__3d_fullres_bs4 |
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- nnUNetTrainerUmamba__nnUNetResEncUNetMPlans__3d_fullres_bs4 |
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These directories correspond to: |
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\1. nnUNetTrainerResenc: The nnU-Net Residual Encoder model with M plans. |
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\2. nnUNetTrainerUmamba: The UMamba model with the proposed modifications. |
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## How to Use |
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Download the trained weights from this repository. |
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Load the model weights into your nnU-Net environment following the standard loading instructions provided by nnU-Net. |
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For more details on the validation performance, refer to the [HNTS-MRG 2024 challenge](https://hntsmrg24.grand-challenge.org/) and the paper. |
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