Efficient Medical Segmentation Anything Model for Robust Lightweight Segmentation under Imperfect Data in Embedded Healthcare.

GroundMed-SAM: Prompt-based Zero-shot Medical Image Segmentation

Medical image segmentation is a key component of computer-aided diagnosis and treatment planning. Despite substantial progress in deep learning–based models, most existing approaches depend heavily on large annotated datasets and often fail to generalize across heterogeneous clinical environments, limiting their deployment in real-world settings characterized by domain shifts and scarce expert annotations. This paper presents a zero-shot learning framework named GroundMed-SAM for medical image segmentation. The framework integrates GroundingDINO for prompt-based region localization and MedSAM for mask generation. To address the weak alignment between visual features and medical semantics in GroundingDINO, which is pretrained on general domain image-text pairs, we introduce learnable medical text embeddings that explicitly parameterize domain-specific terminology in a continuous semantic space. These embeddings are optimized during training to better align medical concepts with visual representations, thereby strengthening text-image correspondence and improving detection-guided segmentation. The proposed framework preserves true zero-shot capability, enabling segmentation of previously unseen anatomical structures without task-specific labels. Extensive experiments on multiple public datasets across diverse modalities and clinical contexts demonstrate that our method achieves competitive segmentation performance in-domain while exhibiting superior robustness under cross-domain evaluation. Although supervised baselines outperform the proposed framework by only 3–5% on in-domain datasets, they experience substantial performance degradation when evaluated on unseen domains. Additionally, the framework achieves an AUC of 98.9 in endoscopic polyp detection, highlighting the effectiveness of the proposed medical-aware textual embeddings in guiding region localization. These results demonstrate the effectiveness of the proposed framework in improving cross-domain generalization for medical image segmentation with limited annotations.

MRD-UNet: a compute-efficient medical image segmentation architecture with multi-attention and parallel dilated fusion

MRD-UNet provides a practical balance between segmentation accuracy and computational efficiency and outperforms baseline CNNs and performs comparably to heavier transformer-based models while using significantly fewer parameters.

Bayesian Adapter-Enhanced SAM-Med2D for Box-Prompted Ultrasound Image Segmentation

Trustworthy Medical Segmentation: Uncertainty-Aware U-Net Evaluation Under Clinical Image Degradation

The results argue for uncertainty-aware inference as a practical safety layer in physician-in-the-loop radiology workflows.

TvaraNet: A Lightweight Mamba Neural Network for Real-Time Medical Image Segmentation

TvaraNet is pro-posed, an extremely lightweight segmentation network designed to preserve boundary fidelity under strict efficiency constraints and achieves competitive or superior boundary-aware performance compared to heavier architectures.

VSS-SAM++: Visual State Space-Aware SAM for 3D Medical Image Segmentation.

The Segment Anything Model (SAM) has demonstrated groundbreaking performance in natural image segmentation, yet its direct application to medical imaging remains suboptimal due to domain shifts in data distributions and the inherent 3D nature of medical data. Although recent SAM-based methods have employed parameter-efficient transfer learning (PETL) to adapt SAM for medical tasks, they often overlook the critical 3D contextual information essential for accurate volumetric segmentation. To address this limitation, we introduce VSS-SAM++, a novel dual-branch architecture that combines SAM's foundational visual priors with Vision Mamba's capacity for modeling long-range spatial dependencies. In this framework, SAM serves as the primary encoder for high-level feature extraction, while a parallel Mamba branch captures cross-slice dependencies in 3D medical volumes. A gated hybrid attention module then dynamically fuses complementary features from both branches, adaptively weighting multi-view representations to minimize feature ambiguity and enhance segmentation precision. Extensive evaluations across nine public CT and MRI datasets demonstrate that VSS-SAM++ outperforms existing methods by 0.2-11.3% in Dice score on multi-organ and lesion segmentation tasks. The framework's robustness to domain shifts and scalability across diverse modalities highlights its potential for clinical deployment.