Cross Modulation and Region Contrast Learning Network for Few-Shot Medical Image Segmentation

Few-shot segmentation is an important approach for mitigating issues related to data annotation and model generalization. However, when applied to medical images, two challenges arise. Firstly, the varying shapes and sizes of different organs result in intra-class variation, and current solutions often introduce a large number of parameters. Secondly, background information similar to the target organ category often leads to inter-class distractors. To address these challenges, this paper proposes a framework consisting of two modules. Among that, using the Cross Modulation Module, the connections between support and query images are explored, thereby enhancing their correlation and reducing the impact of intra-class variation. In the other hand, the Regional Prototype Contrast Module is used to introduce triplet loss to increase separation between prototypes of similar classes in the background and foreground, thereby mitigating the impact of inter-class distractors. Extensive organ segmentation experiments using abdominal magnetic resonance imaging (MRI) and computed tomography (CT) datasets demonstrate that the proposed model meets state-of-the-art performance benchmarks.