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.

提出了一种无需额外训练即可解决未知医学图像分割任务的通用方法，该方法通过使用基于所提出方法的 SAM（Segment Anything Model）图像编码器的一种新型低秩微调策略，与提示编码器和掩膜解码器一起微调标记数据集，实现新任务的泛化。该方法在不同部位的医学图像数据集上进行训练和泛化，并在其他地点数据集上使用心脏图像进行了验证，证明其在未知任务上的优越性能，并对所提方法的重要性能进行了全面分析和总结。