A one-shot/few-shot interactive segmentation method for CT image segmentation

This paper proposes an interactive 3D medical image segmentation method that can deal with limited training dataset. The object boundary prompt simulation strategy and the loss function are carefully designed to train the model. The experimental results show that the performance of the interactive segmentation method remarkably surpasses that of the fully automatic segmentation method, which demonstrates the significant effects of prompt engineering. The experiments also illustrate the moderate costs of applying the interactive segmentation method. In summary, interactive segmentation methods can significantly reduce the time and labor consumed for acquiring precise annotations on the condition of lacking training materials, achieving a better trade-off between model performance and human effort for training the model.