Mamba State Space Model (SSM) Method for the Automatic Segmentation of Organs at Risk from Head and Neck CT Images

Radiotherapy (RT) is one of the main treatment modalities using high-energy radiation to eliminate cancer cells in Head and Neck cancer (HNC) for patients worldwide. Advances such as intensity-modulated radiotherapy (IMRT) improve the precision of targeting tumors and prevent damaging healthy tissues. Segmentation of Organs At Risk (OARs) on Computed Tomography (CT) images is crucial for RT treatment planning in order to avoid damaging healthy structures during the sessions. However, manual delineation of OARs is challenging as their size varies and both small and large organs are present in images with low contrast against the background. In this regard, manual delineation cannot be performed in a timely manner and suffers from potential interobserver variability among clinicians, leading to delays in clinical workflow. In this study, we propose an automatic Deep Learning segmentation framework utilizing effectively the recently introduced Mamba State Space Models (SSM) to localize and delineate multiple OARs with high accuracy from the 3D HNC CT images. Due to the Mamba-based encoder, the model can learn the long-range dependencies of the largest organs by efficiently handling large sequences while also segmenting the smaller ones. The proposed model was evaluated on two public datasets, demonstrating superior performance with an average Dice Similarity Coefficient (DSC) of 75.45% and a Hausdorff distance of 8.22 mm against established deep learning models, such as UNet-based and Transformer-based, in segmenting 9 OARs, indicating promising results to support the clinical workflow.