IEEE Trans Image Process. 2024;33:6204-6215. doi: 10.1109/TIP.2024.3482175. Epub 2024 Oct 30.
Medical image segmentation is a critical task in clinical applications. Recently, the Segment Anything Model (SAM) has demonstrated potential for natural image segmentation. However, the requirement for expert labour to provide prompts, and the domain gap between natural and medical images pose significant obstacles in adapting SAM to medical images. To overcome these challenges, this paper introduces a novel prompt generation method named EviPrompt. The proposed method requires only a single reference image-annotation pair, making it a training-free solution that significantly reduces the need for extensive labelling and computational resources. First, prompts are automatically generated based on the similarity between features of the reference and target images, and evidential learning is introduced to improve reliability. Then, to mitigate the impact of the domain gap, committee voting and inference-guided in-context learning are employed, generating prompts primarily based on human prior knowledge and reducing reliance on extracted semantic information. EviPrompt represents an efficient and robust approach to medical image segmentation. We evaluate it across a broad range of tasks and modalities, confirming its efficacy. The source code is available at https://github.com/SPIresearch/EviPrompt.
医学图像分割是临床应用中的一项关键任务。最近，Segment Anything Model（SAM）在自然图像分割方面显示出了潜力。然而，由于需要专家劳动来提供提示，以及自然图像和医学图像之间存在领域差距，使得将 SAM 应用于医学图像面临着重大挑战。为了克服这些挑战，本文提出了一种名为 EviPrompt 的新型提示生成方法。该方法仅需要一对单参考图像-注释，是一种无需训练的解决方案，显著减少了对广泛标记和计算资源的需求。首先，根据参考图像和目标图像特征之间的相似性自动生成提示，并引入证据学习以提高可靠性。然后，为了减轻领域差距的影响，采用委员会投票和推理引导的上下文学习，主要基于人类先验知识生成提示，减少对提取语义信息的依赖。EviPrompt 是一种高效、稳健的医学图像分割方法。我们在广泛的任务和模态中对其进行了评估，证实了其有效性。源代码可在 https://github.com/SPIresearch/EviPrompt 上获得。
