The scarcity of labeled data poses a significant challenge for deep learning-based medical image segmentation. To address this, this study introduces the novel Foundation Model-based Few-Shot Segmentation (FM-FSS) paradigm. FM-FSS capitalizes on the knowledge distilled from pre-trained foundation models, such as the Segment Anything Model, to enhance segmentation performance in few-shot scenarios. The paradigm designs a feature coupling module that synergizes SAM’s powerful feature extraction capabilities with nnU-Net’s self-configuration strategy, enabling accurate segmentation with minimal labeled data and optional manual prompt inputs. Extensive experiments on a publicly available cardiac CT dataset demonstrate that FM-FSS outperforms state-of-the-art segmentation models. With only 20 labeled images, our method achieves an average Dice score of 94.33% and an ASD of 1.10 mm. Moreover, FM-FSS maintains its label-efficient performance in a one-shot setup, reducing the annotation requirements by at least fourfold. The code and pre-trained models will be released upon acceptance.

Self-Training has proven to be a simple yet effective framework in source-free domain adaptation (SFDA) for medical image segmentation. However, prevalent domain discrepancies and privacy restrictions on source domain data can lead to the negative optimization problem. Current methods predominantly focus on rigorous pseudo-label quality assessment, yet they often overlook the specific prior knowledge inherent to medical images and the potential of online self-training to combat negative optimization. To address these gaps, this paper introduces a selective online self-training method, tackling the issue from both the pseudo-label selection and learning stages. In the selection stage, we identify a domain-invariant prior related to organ shape, and propose a class-prior guided selection mechanism to mitigate class imbalance in pseudo-labels. In the learning stage, we introduce a strategy to selectively update the teacher model based on evolutionary state feedback generated by the student model. Extensive experiments conducted on two widely used benchmarks show the effectiveness of our method, which achieves notable 2.0% and 4.1% Dice improvements.

In this brief, we study the global synchronization of linearly coupled neural networks with delayed couplings, where the intrinsic systems are recurrently connected neural networks with unbounded time-varying delays, and the couplings include instant couplings and unbounded delayed couplings. The concept of micro-synchronization is introduced. Some sufficient conditions are derived for the global micro-synchronization for the underlined coupled systems.