Automated Pseudo-Label Generation and Parallel Computing for Enhanced Few-Shot Medical Image Segmentation

Few-shot semantic segmentation is a technique with significant potential for medical image segmentation tasks. Most existing few-shot semantic segmentation methods require fully annotated labels for the training process. However, these methods may not be suitable for medical images, where data collection and labeling are challenging. To address this issue, this paper proposed an enhanced, few-shot semantic segmentation model with a new pre-processing step to generate pseudo-labels automatically. In this paper, parallel computing is also developed to accelerate image pre-processing. Experiments done on MRI image datasets present the effectiveness of the new approach since it outperforms conventional few-shot semantic segmentation methods.