Attenuation-Resilient Alternating Optimization for Laparoscopic Liver Landmark Detection

Liver surface landmark detection is a fundamental prerequisite for anatomical guidance in laparoscopic liver surgery. However, it remains unreliable in practice due to two pervasive challenges: illumination attenuation in underexposed regions and the structural mismatch between pixel-wise localization and continuous curvilinear geometry. To address these limitations, we propose A2ONet, an attenuation-resilient alternating optimization network for robust liver landmark detection. To mitigate illumination attenuation, A2ONet embraces an illumination field compensation (IFC) block that adaptively enhances dark regions while preserving structural consistency. Meanwhile, we introduce a lightweight frequency-orientation selective filter (FOSF) to suppress repetitive texture interference and preserve salient curvilinear cues. Building upon these resilient representations, we design an alternating seg-curve optimization (ASCO) decoder that iteratively couples dense segmentation with explicit curve modeling, enabling mutual guidance to optimize both structural continuity and endpoint localization. Extensive evaluations on L3D-2K, L3D, and P2ILF demonstrate consistent improvements over competitive methods, establishing a more reliable foundation for intraoperative anatomy guidance. Our code will be available at https://github.com/hyperiondk115/A2ONet.

MMR-Mamba: Multi-Modal MRI Reconstruction with Mamba and Spatial-Frequency Information Fusion

Multi-modal MRI offers valuable complementary information for diagnosis and treatment; however, its utility is limited by prolonged scanning times. To accelerate the acquisition process, a practical approach is to reconstruct images of the target modality, which requires longer scanning times, from under-sampled k-space data using the fully-sampled reference modality with shorter scanning times as guidance. The primary challenge of this task is comprehensively and efficiently integrating complementary information from different modalities to achieve high-quality reconstruction. Existing methods struggle with this: 1) convolution-based models fail to capture long-range dependencies; 2) transformer-based models, while excelling in global feature modeling, struggle with quadratic computational complexity. To address this, we propose MMR-Mamba, a novel framework that thoroughly and efficiently integrates multi-modal features for MRI reconstruction, leveraging Mamba's capability to capture long-range dependencies with linear computational complexity while exploiting global properties of the Fourier domain. Specifically, we first design a Target modality-guided Cross Mamba (TCM) module in the spatial domain, which maximally restores the target modality information by selectively incorporating relevant information from the reference modality. Then, we introduce a Selective Frequency Fusion (SFF) module to efficiently integrate global information in the Fourier domain and recover high-frequency signals for the reconstruction of structural details. Furthermore, we devise an Adaptive Spatial-Frequency Fusion (ASFF) module, which mutually enhances the spatial and frequency domains by supplementing less informative channels from one domain with corresponding channels from the other.

MMR-Mamba: Multi-Contrast MRI Reconstruction with Mamba and Spatial-Frequency Information Fusion

Multi-contrast MRI acceleration has become prevalent in MR imaging, enabling the reconstruction of high-quality MR images from under-sampled k-space data of the target modality, using guidance from a fully-sampled auxiliary modality. The main crux lies in efficiently and comprehensively integrating complementary information from the auxiliary modality. Existing methods either suffer from quadratic computational complexity or fail to capture long-range correlated features comprehensively. In this work, we propose MMR-Mamba, a novel framework that achieves comprehensive integration of multi-contrast features through Mamba and spatial-frequency information fusion. Firstly, we design the \textit{Target modality-guided Cross Mamba} (TCM) module in the spatial domain, which maximally restores the target modality information by selectively absorbing useful information from the auxiliary modality. Secondly, leveraging global properties of the Fourier domain, we introduce the \textit{Selective Frequency Fusion} (SFF) module to efficiently integrate global information in the frequency domain and recover high-frequency signals for the reconstruction of structure details. Additionally, we present the \textit{Adaptive Spatial-Frequency Fusion} (ASFF) module, which enhances fused features by supplementing less informative features from one domain with corresponding features from the other domain. These innovative strategies ensure efficient feature fusion across spatial and frequency domains, avoiding the introduction of redundant information and facilitating the reconstruction of high-quality target images. Extensive experiments on the BraTS and fastMRI knee datasets demonstrate the superiority of the proposed MMR-Mamba over state-of-the-art MRI reconstruction methods.