Medical image segmentation based on adaptive threshold prediction and feature fusion

Abstract

In the realm of computer-aided diagnosis, image segmentation techniques have garnered significant attention over the years. However, medical image data present distinctive characteristics, such as confidentiality, class imbalance, and challenges in acquiring accurate labels. Of certain rare medical conditions, it is arduous to obtain an adequate number of data samples within a limited scope for training models. Moreover, complex medical conditions may lead to diagnostic omissions concerning the locations of minor lesions. Consequently, our primary objective in this article is to address the challenge of enhancing the segmentation accuracy of lesion locations in complex medical conditions when working with limited data. Additionally, we believe that threshold selection and feature choice are particularly crucial for medical image segmentation tasks. However, existing medical image segmentation methods often prioritizes model complexity while overlooking the impact of threshold variations on segmentation performance. In this paper, we propose a multi-feature fusion network with adaptive threshold prediction and introduce a novel paradigm of semi-supervised pseudo-label generation to facilitate supervised learning. The multi-feature fusion module ensures the comprehensive utilization of dimension-sensitive features, while the adaptive threshold predictor enables the model to generate distinct segmentation thresholds based on varying inputs. We employed publicly available medical image datasets, namely Kvasir-SEG, Rim-one r2, and ISIC 2018, to train and validate our model and approach. In comparison to other supervised and semi-supervised methods, the model and the approach we proposed demonstrate superior segmentation performance.