Automated lesion detection in chest X-rays has demonstrated significant potential for improving clinical diagnosis by precisely localizing pathological abnormalities. While recent promptable detection frameworks have achieved remarkable accuracy in target localization, existing methods typically rely on manual annotations as prompts, which are labor-intensive and impractical for clinical applications. To address this limitation, we propose SP-Det, a novel self-prompted detection framework that automatically generates rich textual context to guide multi-label lesion detection without requiring expert annotations. Specifically, we introduce an expert-free dual-text prompt generator (DTPG) that leverages two complementary textual modalities: semantic context prompts that capture global pathological patterns and disease beacon prompts that focus on disease-specific manifestations. Moreover, we devise a bidirectional feature enhancer (BFE) that synergistically integrates comprehensive diagnostic context with disease-specific embeddings to significantly improve feature representation and detection accuracy. Extensive experiments on two chest X-ray datasets with diverse thoracic disease categories demonstrate that our SP-Det framework outperforms state-of-the-art detection methods while completely eliminating the dependency on expert-annotated prompts compared to existing promptable architectures.

The universality of deep neural networks across different modalities and their generalization capabilities to unseen domains play an essential role in medical image segmentation. The recent segment anything model (SAM) has demonstrated strong adaptability across diverse natural scenarios. However, the huge computational costs, demand for manual annotations as prompts and conflict-prone decoding process of SAM degrade its generalization capabilities in medical scenarios. To address these limitations, we propose a modality-decoupled lightweight SAM for domain-generalized medical image segmentation, named De-LightSAM. Specifically, we first devise a lightweight domain-controllable image encoder (DC-Encoder) that produces discriminative visual features for diverse modalities. Further, we introduce the self-patch prompt generator (SP-Generator) to automatically generate high-quality dense prompt embeddings for guiding segmentation decoding. Finally, we design the query-decoupled modality decoder (QM-Decoder) that leverages a one-to-one strategy to provide an independent decoding channel for every modality, preventing mutual knowledge interference of different modalities. Moreover, we design a multi-modal decoupled knowledge distillation (MDKD) strategy to leverage robust common knowledge to complement domain-specific medical feature representations. Extensive experiments indicate that De-LightSAM outperforms state-of-the-arts in diverse medical imaging segmentation tasks, displaying superior modality universality and generalization capabilities. Especially, De-LightSAM uses only 2.0% parameters compared to SAM-H. The source code is available at https://github.com/xq141839/De-LightSAM.

Medical image analysis is critical yet challenged by the need of jointly segmenting organs or tissues, and numerous instances for anatomical structures and tumor microenvironment analysis. Existing studies typically formulated different segmentation tasks in isolation, which overlooks the fundamental interdependencies between these tasks, leading to suboptimal segmentation performance and insufficient medical image understanding. To address this issue, we propose a Co-Seg++ framework for versatile medical segmentation. Specifically, we introduce a novel co-segmentation paradigm, allowing semantic and instance segmentation tasks to mutually enhance each other. We first devise a spatio-sequential prompt encoder (SSP-Encoder) to capture long-range spatial and sequential relationships between segmentation regions and image embeddings as prior spatial constraints. Moreover, we devise a multi-task collaborative decoder (MTC-Decoder) that leverages cross-guidance to strengthen the contextual consistency of both tasks, jointly computing semantic and instance segmentation masks. Extensive experiments on diverse CT and histopathology datasets demonstrate that the proposed Co-Seg++ outperforms state-of-the-arts in the semantic, instance, and panoptic segmentation of dental anatomical structures, histopathology tissues, and nuclei instances. The source code is available at https://github.com/xq141839/Co-Seg-Plus.

Accurate 3D vascular segmentation is essential for diagnosing and treating vascular diseases. This task remains challenging due to the complexity of the 3D data and the morphological diversity of blood vessels. In recent years, state space models (SSMs) have received a great attention for its good performance while preserving global receptive field and consuming less computing resources and time. Inspired by this, we propose a model called Swin-VasMamba for 3D vascular segmentation. It consists of a network called CMU-Net and a topologically constrained loss function called dsh loss. We compare our model with several other advanced segmentation models based on CNN, Transformer and Mamba. The results show that Swin-VasMamba achieves a state-of-the-art performance, with the highest Dice coefficient of 0.880, the lowest 95th-percentile of Hausdorff Distance (HD95) of 0.673, and the lowest Average Surface Distance (ASD) of 0.159 on a benchmark dataset.

Ultrasound imaging is the most commonly used method for screening thyroid nodules due to its low cost and non-invasive nature. Thyroid nodule lesions have variable shapes, rich aspect ratios, unclear boundaries, calcified nodule-induced acoustic shadows, and noise interference, causing challenges in accurate segmentation. Recent methods ignore various scale features and details in different resolutions of images, leading to redundant or missing feature information and then affecting the segmentation performance. In this paper, we introduce a hybrid and full attention UNet model for ultrasound thyroid nodule segmentation. Self, spatial and channel attention are combined in a U-Net-like structure to extract global and local features simultaneously. A novel full attention multi-scale fusion stage is designed to enhance boundary features while suppressing noise features. At the same time, the model dynamically adjusts the number of skip connections corresponding to images of different resolutions to better utilize multi-scale features and detailed information. We evaluate our model on DDTI, TN3K and Stanford Cine-Clip datasets, including internal validation and cross-dataset testing. The results show that our proposed model for internal validation in the DDTI dataset increases the Dice score and mean intersection over union by 2.36 % and 1.04 % compared to the state-of-the-art model. In the TN3K dataset, they increase by 1.66 % and 3.05 %.

As the vision foundation models like the Segment Anything Model (SAM) demonstrate potent universality, they also present challenges in giving ambiguous and uncertain predictions. Significant variations in the model output and granularity can occur with simply subtle changes in the prompt, contradicting the consensus requirement for the robustness of a model. While some established works have been dedicated to stabilizing and fortifying the prediction of SAM, this paper takes a unique path to explore how this flaw can be inverted into an advantage when modeling inherently ambiguous data distributions. We introduce an optimization framework based on a conditional variational autoencoder, which jointly models the prompt and the granularity of the object with a latent probability distribution. This approach enables the model to adaptively perceive and represent the real ambiguous label distribution, taming SAM to produce a series of diverse, convincing, and reasonable segmentation outputs controllably. Extensive experiments on several practical deployment scenarios involving ambiguity demonstrates the exceptional performance of our framework. Project page: \url{https://a-sa-m.github.io/}.