Self-Prompting Segment Anything Model for Few-Shot Medical Image Segmentation

Abstract

Segmenting unlabelled medical images with a minimal amount of labelled data is a daunting task due to the complex feature landscapes and the prevalent noise and artefacts characteristic of medical imaging processes. The SAM has showcased the potential of large-scale image segmentation models for achieving zero-shot generalisation across previously unseen objects. However, directly applying SAM to medical image segmentation without incorporating prior knowledge of the target task can lead to unsatisfactory results. To address this, we enhance SAM by integrating prior knowledge of medical image segmentation tasks. This enables it to quickly adapt to few-shot medical image segmentation tasks while ensuring efficient parameter training. Our method employs an ensemble learning strategy to train a simple classifier, producing a coarse mask for each test image. Importantly, this coarse mask generates more accurate prompt points and boxes, thus improving SAM's capacity for prompt-driven segmentation. Furthermore, to refine SAM's ability to produce more precise masks, we introduce the Isolated Noise Removal (INR) module, which efficiently removes noise from the coarse masks. In addition, our novel Multi-point Automatic Prompt (MPAP) module is designed to independently generate multiple effective and evenly distributed point prompts based on these coarse masks. Additionally, we introduce an innovative knee joint dataset benchmark specifically for medical image segmentation, contributing further to the research field. Extensive evaluations on three benchmark datasets confirm the superior performance of our approach compared to existing methods, demonstrating its efficacy and significant progress in the domain of few-shot medical image segmentation.