Accurate and temporally consistent segmentation of the left ventricle from echocardiography videos is essential for estimating the ejection fraction and assessing cardiac function. However, modeling spatiotemporal dynamics remains difficult due to severe speckle noise and rapid non-rigid deformations. Existing linear recurrent models offer efficient in-context associative recall for temporal tracking, but rely on unconstrained state updates, which cause progressive singular value decay in the state matrix, a phenomenon known as rank collapse, resulting in anatomical details being overwhelmed by noise. To address this, we propose OSA, a framework that constrains the state evolution on the Stiefel manifold. We introduce the Orthogonalized State Update (OSU) mechanism, which formulates the memory evolution as Euclidean projected gradient descent on the Stiefel manifold to prevent rank collapse and maintain stable temporal transitions. Furthermore, an Anatomical Prior-aware Feature Enhancement module explicitly separates anatomical structures from speckle noise through a physics-driven process, providing the temporal tracker with noise-resilient structural cues. Comprehensive experiments on the CAMUS and EchoNet-Dynamic datasets show that OSA achieves state-of-the-art segmentation accuracy and temporal stability, while maintaining real-time inference efficiency for clinical deployment. Codes are available at https://github.com/wangrui2025/OSA.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Automated 3D pulmonary vessel segmentation from CT images is crucial for improving early screening and assessment of pulmonary vessel related diseases. However, it remains an extremely challenging task due to the complex and tree-like structures of vessels, large scale-variations, and the existence of highly similar tissues in the background. Existing segmentation models either cannot sufficiently capture long-range structural dependencies, which are of great importance in vessel segmentation, or are constrained by insufficient computational resources in clinical settings. In this paper, we propose VesMamba, a novel model for 3D pulmonary vessel segmentation that comprehensively addresses these challenges. Specifically, we first devise a spatial-gated structural perception (SSP) module, which employs Mamba to efficiently capture long-range dependencies. In SSP, we design dynamic spatial attention convolutions (DSAC) for dynamically learning the tree-like 3D vessel structures, providing Mamba with the spatial perception capability to better track the complicated topologies of vessels. Second, we propose an innovative bidirectional scale-aware filter (BSF) module to strengthen the representation capability of the encoder, facilitating our model to focus on vessels of different scales under noise. Moreover, we apply a mask-constrained decoder to further improve segmentation consistency and accuracy, which constrains the inference of adjacent low-layer decoders directly by high-layer masks. Extensive experiments on the public Parse22 and internal Lung79 datasets demonstrate that our method can achieve better performance than SOTAs. Code is available at https://github.com/Lzpbright/VesMamba.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Intraoperative bleeding in laparoscopic surgery causes rapid obscuration of the operative field to hinder the surgical process and increases the risk of postoperative complications. Intelligent detection of bleeding areas can quantify the blood loss to assist decision-making, while locating bleeding points helps surgeons quickly identify the source of bleeding and achieve hemostasis in time to improve surgical success rates. To fill the benchmark gap, we first construct a real-world laparoscopic surgical bleeding detection dataset, named SurgBlood, comprising 5,330 frames from 95 surgical video clips with bleeding region and point annotations. Accordingly, we develop a dual-task synergistic online detector called BlooDet, enabling simultaneous detection of bleeding regions and points in laparoscopic surgery. The baseline embraces a dual-branch bidirectional guidance design based on Segment Anything Model 2. The mask branch detects bleeding regions through adaptive edge and point prompt embeddings, while the point branch leverages mask memory to induce bleeding point memory modeling and captures point motion direction via inter-frame optical flow. By coupled bidirectional guidance, our framework explores spatial-temporal correlations while exploiting memory modeling to infer current bleeding status. Extensive experiments indicate that our method outperforms 13 counterparts in bleeding detection. Code and data are available at https://github.com/PJLallen/SurgBlood.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Automatic and accurate echocardiography video segmentation is essential for efficient and repeatable measurements of key clinical functional indicators for the diagnosis of cardiovascular diseases. However, it is an extremely challenging task to obtain high-quality segmentation results throughout the cardiac cycle owing to (1) the inherent speckle noise in echocardiography videos, (2) the complex dynamic motions of cardiac structures, and (3) the scarcity of annotated data. To comprehensively address these challenges, we propose a novel semi-supervised model, EchoForge, which can achieve accurate and real-time echocardiography video segmentation with very limited annotations. EchoForge introduces an Anchor Semantic Awareness (ASA) module that refines ambiguous regions using learnable anchors and propagates structural prototypes across frames to enhance boundary delineation and temporal consistency. Building upon ASA, a Continuous Pseudo-label Reforging (CPR) module progressively integrates and refines pseudo-labels via channel-wise attention, providing robust supervision. Extensive experiments on the CAMUS and EchoNet-Dynamic benchmarks demonstrate that EchoForge outperforms state-of-the-art (SOTA) methods in accuracy while maintaining real-time efficiency. The code is available at https://github.com/YunPeng-Fang/EchoForge.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表