부산대학교 도서관

Automatic and precise organ segmentation plays a significant role in promoting the development of the diagnosis and treatment of the disease. Despite making enormous strides in medical image segmentation, conventional deep neural network-based methods are inherently massive data-driven techniques and are challenging to adapt to novel classes with a small number of labeled samples. Few-shot learning is a promising solution through learning novel classes from extremely limited annotated examples. However, existing few-shot segmentation methods focus excessively on targets in individual images while neglecting to model the global spatial correlation across images, which may cause severe performance degradation. To solve this issue, we propose a new Transformer-based few-shot segmentation framework for medical imaging, namely location-aware transformer network (LATNet), which establishes the spatial correlation between support and query objects, yielding location-aware prototypes, and then performs segmentation by computing the semantic similarity between query features and location-aware prototypes. Additionally, to further enhance the representativeness of the obtained location-aware prototypes in low-data regimes, we design a prediction iterative refinement module, which can iteratively exploit the query predictions output by each iteration to update the location-aware prototypes and progressively refine the query predictions. Extensive experiments on three challenging medical image datasets, i.e., Abd-MRI, Card-MRI, and Abd-CT, show that the proposed LATNet achieves remarkable improvements over current state-of-the-art methods by an average of 4.17%, 1.50%, and 4.63% in terms of the Dice Score, respectively.