Although deep learning models have become the main method for medical image segmentation, they often cannot be extended to unknown segmentation tasks involving new anatomical structures, image shapes, or labels. For new segmentation tasks, researchers often have to retrain or fine-tune the model, which is time-consuming and poses a significant obstacle to clinical researchers, who often lack the resources and professional knowledge to train neural networks. Therefore, we proposed a general method that can solve unknown medical image segmentation tasks without requiring additional training. Given an example set of images and prompts for defining new segmentation tasks, GMISeg applies a novel low-rank fine-tuning strategy based on the proposed approach to the SAM (Segment Anything Model) image encoder, and works with the prompt encoder and mask decoder to fine-tune the labeled dataset without the need for additional training. To achieve generalization of new tasks, we used medical image datasets with different imaging modes for different parts. We trained and generalized GMISeg on a different set of anatomical and imaging modes using cardiac images on other site datasets. We have demonstrated that GMISeg outperforms the latest methods on unknown tasks and have conducted a comprehensive analysis and summary of the important performance of the proposed method.

翻译：尽管深度学习模型已成为医学图像分割的主要方法，但它们通常无法推广到涉及新解剖结构、图像形状或标签的未知分割任务。对于新的分割任务，研究人员通常需要重新训练或微调模型，这既耗时，也对缺乏训练神经网络所需资源和专业知识的临床研究人员构成重大障碍。因此，我们提出了一种通用方法，可以在无需额外训练的情况下解决未知的医学图像分割任务。给定一组用于定义新分割任务的示例图像和提示，GMISeg基于所提出的方法对SAM（Segment Anything Model）图像编码器应用一种新颖的低秩微调策略，并与提示编码器和掩码解码器协同工作，在无需额外训练的情况下对标记数据集进行微调。为了实现新任务的泛化，我们使用了针对不同部位的不同成像模式的医学图像数据集。我们使用心脏图像和其他部位数据集，在不同解剖结构和成像模式组合上对GMISeg进行训练与泛化。我们证明了GMISeg在未知任务上优于最新方法，并对所提方法的重要性能进行了全面分析与总结。