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

GMISeg: General Medical Image Segmentation without Re-Training