He Ma

MFFUNet: A hybrid model with cross-attention-guided multi-feature fusion for automated segmentation of organs at risk in cervical cancer brachytherapy

Brachytherapy is a common treatment option for cervical cancer. An important step involved in brachytherapy is the delineation of organs at risk (OARs) based on computed tomography (CT) images. Automating OARs segmentation in brachytherapy has the benefit of both reducing the time and improving the quality of radiation therapy planning. This paper introduces a novel segmentation model named MFFUNet for the automatic contour delineation of OARs in cervical cancer brachytherapy. The proposed model employs a staged encoder-decoder structure, integrating the self-attention mechanism of Transformer with the CNN framework. A novel multi-features fusion (MFF) block with a cross-attention-guided feature fusion mechanism is also proposed, which efficiently extracts and cross-fuses features from multiple receptive fields, enriching the semantic information of the features and thus improving the performance of complex segmentation tasks. A private CT image dataset of 95 patients with cervical cancer undergoing brachytherapy is used to evaluate the segmentation performance of the proposed method. The OARs in the data consist of the bladder, rectum, and colon surrounding the cervix. The proposed model surpasses current mainstream OARs segmentation models in terms of segmentation accuracy. The mean Dice similarity coefficient (DSC) score of all three OARs has achieved 73.69%. Among them, the DSC score for the bladder is 92.65%, for the rectum is 66.55%, and for the colon is 61.86%. Moreover, we also conducted experiments on two common public thoracoabdominal multi-organ CT datasets. The excellent segmentation performance further demonstrates the generalization ability of our model. In conclusion, MFFUNet has demonstrated outstanding effectiveness in segmenting OARs for cervical cancer brachytherapy. By accurately delineating OARs, it enhances radiotherapy planning precision and helps reduce radiation toxicity, improving patient outcomes.