Colposcopic Image Segmentation Based on Feature Refinement and Attention

The current computer-aided diagnosis for cervical cancer screening encounters issues with missing detailed information during colposcopic image segmentation and incomplete edge delineation. To overcome these challenges, this study introduces the RUC-U2Net architecture, which enhances image segmentation through feature refinement and upsampling connections. Two variants are developed: RUC-U2Net and the lightweight RUC+-U2Net. Initially, a feature refinement module that leverages an attention mechanism is proposed to improve detail capture by the model's fundamental unit during downsampling. Subsequently, the integration of diagonal attention in connecting peer-level encoders and decoders supplements finer semantic details to the decoder's feature maps, addressing the problem of incomplete edge segmentation. Finally, the application of the Focal Tversky loss function allows the model to concentrate on difficult samples, mitigating the challenges posed by imbalanced distributions of positive and negative samples in training datasets. Experimental evaluations on three publicly available datasets demonstrate that the proposed models significantly outperform existing methods across seven performance metrics, evidencing their superior segmentation accuracy.