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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



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/).

Preprocessing, postprocessing and model codes can be found at [UMambaAdj Github](https://github.com/Aarhus-RadOnc-AI/UMambaAdj).

## Available Model Weights

The trained weights and validation results are stored in the following directories:
- nnUNetTrainerResenc__nnUNetResEncUNetMPlans__3d_fullres_bs4
- nnUNetTrainerUmamba__nnUNetResEncUNetMPlans__3d_fullres_bs4

These directories correspond to:
\1. nnUNetTrainerResenc: The nnU-Net Residual Encoder model with M plans.
\2. nnUNetTrainerUmamba: The UMamba model with the proposed modifications.

## How to Use

Download the trained weights from this repository.
Load the model weights into your nnU-Net environment following the standard loading instructions provided by nnU-Net.


For more details on the validation performance, refer to the [HNTS-MRG 2024 challenge](https://hntsmrg24.grand-challenge.org/) and the paper.