  Accurate segmentation of lesion regions is crucial for clinical diagnosis and
treatment across various diseases. While deep convolutional networks have
achieved satisfactory results in medical image segmentation, they face
challenges such as loss of lesion shape information due to continuous
convolution and downsampling, as well as the high cost of manually labeling
lesions with varying shapes and sizes. To address these issues, we propose a
novel medical visual prompting (MVP) framework that leverages pre-training and
prompting concepts from natural language processing (NLP). The framework
utilizes three key components: Super-Pixel Guided Prompting (SPGP) for
superpixelating the input image, Image Embedding Guided Prompting (IEGP) for
freezing patch embedding and merging with superpixels to provide visual
prompts, and Adaptive Attention Mechanism Guided Prompting (AAGP) for
pinpointing prompt content and efficiently adapting all layers. By integrating
SPGP, IEGP, and AAGP, the MVP enables the segmentation network to better learn
shape prompting information and facilitates mutual learning across different
tasks. Extensive experiments conducted on five datasets demonstrate superior
performance of this method in various challenging medical image tasks, while
simplifying single-task medical segmentation models. This novel framework
offers improved performance with fewer parameters and holds significant
potential for accurate segmentation of lesion regions in various medical tasks,
making it clinically valuable.
