Key information-guided networks for medical image segmentation in medical systems

Motivated by the goals of overcoming inherent engineering difficulties on segmentation accuracy arising from low contrast and complex tissue relationships in medical images, this paper investigates how key information can be extracted and analyzed in a targeted manner to discover relationships between tissues and enhance feature differentiation. It proposes the Key Information-guided segmentation algorithm, which consists of two key components: the Key Region-guided Sparse Self-Attention (KRSSA) module and the Hard Pixels Multisubclass Contrast learning (HPMCL) module. The former is applied in the encoder by activating the importance of global features and adaptively selecting attentional regions. Experimental results show that propagating large amounts of complex and redundant information suppresses learning. The latter is applied in the final decision layer, where the feature space of each class is partitioned into several subclasses, and hard pixels are continuously pulled towards the closest subclass of their respective class, away from all subclasses of other classes by contrastive learning. Experimental results show that moving pixels towards subclasses rather than a single class center leads to more effective segmentation results. In summary, we propose a novel visual model and extensively validate it on the Brain Tumor Segmentation 2019 Challenge (BraTS2019) dataset, BraTS2020 dataset, and the Medical Segmentation Decathlon (MSD) dataset, namely in brain tumors and the hippocampus. The Dice similarity coefficients of 0.806, 0.904, and 0.853 for the enhanced tumor, whole tumor, and tumor core, respectively, in the Brats2020 validation set ranked fourth out of 345 participating methods. This method has significant implications for addressing challenges in medical image segmentation, including low attention to key regions, class imbalance, and background interference.