Medical Visual Prompting (MVP): A Unified Framework for Versatile and High-Quality Medical Image Segmentation

• URL: http://arxiv.org/abs/2404.01127v1
• Date: Mon, 1 Apr 2024 14:06:48 GMT
• Title: Medical Visual Prompting (MVP): A Unified Framework for Versatile and High-Quality Medical Image Segmentation
• Authors: Yulin Chen, Guoheng Huang, Kai Huang, Zijin Lin, Guo Zhong, Shenghong Luo, Jie Deng, Jian Zhou,
• Abstract summary: We propose a medical visual prompting (MVP) framework that leverages pre-training and prompting concepts from natural language processing (NLP) The MVP enables the segmentation network to better learn shape prompting information and facilitates mutual learning across different tasks. This novel framework offers improved performance with fewer parameters and holds significant potential for accurate segmentation of lesion regions in various medical tasks.
• Score: 15.460598807078751
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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