Volume 45, Issue 8

4D (3D over time) fetal heart reconstruction improves detection and functional assessment of congenital malformations compared with 2D methods, but remains challenging due to the lack of publicly available 4D echocardiography datasets, the burden of full 3D/4D annotations, and the computational cost of volumetric networks. To address these challenges, we introduce a 2.5D radial-slicing paradigm th...

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Multi-source computed tomography (MSCT) significantly improves temporal resolution but suffers from severe forward and cross scatter artifacts. Software-based scatter correction methods avoid additional hardware costs and radiation dose; however, model-based methods struggle with high-order scatter estimation, while deep learning–based methods lack physical constraints. To address these limitation...

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Medical Referring Image Segmentation (MRIS) involves segmenting target regions in medical images based on natural language descriptions. While achieving promising results, recent approaches usually involve complex design of multimodal fusion or multi-stage decoders. In this work, we propose NTP-MRISeg, a novel framework that reformulates MRIS as an autoregressive next-token prediction task over a ...

中文摘要翻译待生成

Precise segmentation of medical images plays a crucial role in modern clinical practice, providing important foundations for the quantitative analysis of medical images and clinical decision making. However, although deep learning techniques have achieved significant success in conventional medical image segmentation, they still exhibit obvious limitations when faced with complex structure segment...

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Magnetic resonance imaging (MRI) is highly susceptible to patient motion due to its relatively long acquisition times and the fact that data are acquired sequentially in k-space. Even small patient movements introduce phase inconsistencies across measurements, leading to severe artifacts such as blurring, ghosting, and geometric distortions that can compromise diagnostic quality. Retrospective mot...

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Accurate risk prediction and early diagnosis are crucial for the early intervention of Alzheimer's disease (AD). Current prediction models usually have limited power in capturing the complex interplays between the heterogeneous inputs or lack biological explainability required for clinical adoption and new diagnosis biomarkers discovery. Inspired by pioneering works on biologically informed networ...

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Mixture-of-Experts (MoE) architectures achieve scalable learning by routing inputs to specialized subnetworks through conditional computation. However, conventional MoE designs assume homogeneous expert capability and domain-agnostic routing—assumptions that are fundamentally misaligned with medical imaging, where anatomical structure and regional disease heterogeneity govern pathological patterns...

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Functional neuroimaging with ultrafast ultrasound is an emerging neuroimaging tool for studying neural activities in the rodent brain. Existing methods, however, are challenged by the compromise between functional imaging sensitivity (i.e., sensitivity in detecting neural responses) and spatial resolution. For example, functional ultrasound (fUS) uses native red blood cells (RBCs) as imaging targe...

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Contrast-free myocardial infarction (MI) segmentation is essential for mitigating the health risks associated with contrast agents (CAs) in clinical diagnostics. However, existing approaches are limited by their reliance on strictly paired CINE sequences and contrastenhanced images, which are often difficult to obtain because patient conditions and imaging protocols often cause inter-modality slic...

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Coronary artery stenosis is a leading cause of cardiovascular disease, diagnosed by analyzing the coronary arteries from multiple angiography views. Although numerous deep-learning models have been proposed for stenosis detection from a single angiography view, their performance heavily relies on expensive view-level annotations, which are often not readily available in hospital systems. Moreover, these models fail to capture the temporal dynamics and dependencies among multiple views, which are crucial for clinical diagnosis. To address this, we propose SegmentMIL, a transformer-basedmulti-viewmultiple-instance learning framework for patient-level stenosis classification. Trained on a real-world clinical dataset, using patient-level supervision and without any view-level annotations, SegmentMIL jointly predicts the presence of stenosis and localizes the affected anatomical region, distinguishing between the right and left coronary arteries and their respective segments. SegmentMIL obtains high performance on internal and external evaluations and outperforms both view-level models and classical MIL baselines, underscoring its potential as a clinically viable and scalable solution for coronary stenosis diagnosis. Our code is available at https://github.com/NikolaCenic/mil-stenosis.

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Accurate segmentation of cardiac MRI is essential for assessment of cardiac function through biomarkers such as the left and right ventricular ejection fraction (LVEF, RVEF). Although AI methods have achieved high average segmentation accuracy, the precision of biomarkers for individual patients – quantified by estimation variance, remains critical for reliable diagnosis. Calibrated biomarkers, whose uncertainty accurately reflects the true variability, are highly desirable. However, existing evaluations predominantly focus on population-level segmentation accuracy, leaving biomarker-level uncertainty and calibration largely underexplored. Intrinsic anatomical ambiguity and annotation variability are major sources of biomarker variability and cannot be fully eliminated, even when training on a single annotation set. To address this, we propose a probabilistic segmentation framework that explicitly models aleatoric uncertainty with the goal of improving calibration in the biomarker space. The framework disentangles two key sources of uncertainty: (1) detection uncertainty , arising from ambiguous inclusion of basal or apical slices in 2D cardiac MRI, modeled via objectness probabilities; and (2) contour uncertainty , reflecting variability in ventricular boundary delineation, modeled through mean–variance regression of elliptic Fourier descriptors, a compact representation of closed contours. By propagating these uncertainties to derived biomarkers, the proposed method produces more informative and better-calibrated confidence estimates for ejection fraction. Compared to conventional pixel-wise approaches, our framework improves biomarker reliability, particularly in realistic settings dominated by annotation ambiguity and limited domain shift.

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Osteoarthritis (OA) is a debilitating joint disease in which early microstructural and compositional changes in articular cartilage (AC) are challenging to detect with current diagnostic tools. We hypothesized that multispectral imaging (MSI) could capture optical signatures that predict key AC properties. To test this, we developed and evaluated an MSI-based approach for estimating tissue thickness, proteoglycan (PG) content, collagen fiber orientation, and cell morphological properties (area and circularity). Reflectance images of bovine patellar AC were acquired using a custom-built MSI system operating at six wavelengths (550–970 nm), and machine learning models were trained to predict the targeted markers. The models yielded reliable estimates for AC thickness, whereas for PG content, collagen fiber orientation, and cell circularity, they achieved moderate accuracy. This work suggests that MSI holds promise as a label-free tool for characterizing AC and detecting early degenerative changes.

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Limited-angle cone-beam computed tomography (LA-CBCT) enables rapid imaging and reduced radiation exposure, but its severely incomplete projection data lead to ill-posed reconstructions with prominent artifacts, limiting clinical applicability. Recent advances in 3D Gaussian Splatting (3D-GS) have shown promise for efficient tomographic reconstruction, yet its performance remains highly sensitive to initialization. In this work, we present SPARK (Structurally-Informed Projection-Accelerated Reconstruction), a two-stage framework that introduces a generative, structurally informed initialization for 3D-GS. In the first stage, a geometry-conditioned network directly predicts complete 3D Gaussian parameters from a sparse subset of projections, embedding learned anatomical priors to mitigate artifact propagation. In the second stage, the generated scene is refined through physics-based 3D-GS optimization, yielding high-fidelity reconstructions consistent with measured projections. Extensive experiments on public datasets demonstrate that SPARK substantially improves both image quality and convergence speed, achieving superior PSNR/SSIM in severely limited-angle scenarios compared with analytical, iterative, and deep learning baselines. Moreover, SPARK reconstructions provide enhanced inputs for downstream post-processing networks, further boosting image fidelity. These results suggest that SPARK is a promising prior-informed 3D-GS framework for simulated LA-CBCT reconstruction under limited angular coverage, providing an effective bridge between data-driven anatomical priors and physics-based projection-domain refinement.

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Accurate Cobb angle measurement is essential for scoliosis assessment but remains labor-intensive and observer-dependent. We introduce DGPDT (Detection-Guided Prompt-driven Transformer), a unified transformer-based framework that integrates vertebra detection and foundation-model segmentation for generalizable spinal analysis. A Roboflow Detection Transformer (RF-DETR) with a DINOv2 backbone localizes vertebrae, followed by post-processing to ensure anatomical continuity. The resulting bounding boxes serve as automatic prompts for a fine-tuned Segment Anything Model 2.1 (SAM 2.1), which generates high-resolution vertebral masks. Cobb angles are then computed from vertebral masks, enabling estimation of both main and compensatory curves. Evaluations on the in-house (TVGH-SpineXR) and external (SpineWeb-16) datasets demonstrate encouraging performance on both internal and external datasets, achieving mean Dice coefficients of 0.944 and 0.781, respectively, and mean absolute Cobb angle errors of approximately 2–3° in-domain and 4.93° under cross-domain evaluation. Despite being trained solely on TVGH-SpineXR, DGPDT maintains accuracy comparable to models trained directly on the benchmark dataset. By coupling detection-guided prompting with transformer-based segmentation, DGPDT achieves a clinically acceptable mean absolute error (<5°), suggesting good reproducibility and potential applicability beyond the training dataset.

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Breast density impacts cancer detection by masking tumors within fibroglandular tissue and there are disparities in screening outcomes across racial groups. However, it remains unclear whether these differences reflect inherent tissue characteristics or systemic bias. To isolate the effect of breast density on lesion detectability across racial subgroups, we conducted a retrospective case-control study using raw tomosynthesis projections from 902 women (453 cases, 451 matched controls) across BI-RADS density categories and self-reported race. Identical in-silico spiculated masses (8–15 mm) and microcalcification clusters (10–14 mm) were inserted into the projections using a calibrated lesion model. Images were reconstructed in the same manner to avoid proprietary processing in lesion detection. Lesion detectability was assessed with Channelized Hotelling Observers. Regression and causal mediation analyses examined the relationships between race, density, and detectability. As result, detectability decreased with increasing density; for masses, the area under the receiver operating characteristic curve (ROC AUC) reduced significantly from 0.93 to 0.85 (BIRADS A to D), whereas for microcalcifications AUC decreased from 0.85 to 0.78 across the same density range. Discrimination remained higher for masses than calcifications (AUC=0.89 vs. AUC=0.82). Stratified analyses showed slightly higher detectability in Non-Hispanic Black women compared with Non-Hispanic White and Asian American women, largely reflecting differences in density. Mediation analysis revealed that breast density accounted for 38–55% of the observed race-associated detectability differences. Mediation analyses have shown that density is the dominant factor in detectability. These findings support the development of calibrated detection models and personalized screening strategies that account for breast density.

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Whole Slide Imaging (WSI) has become a gold standard in cancer diagnosis, inspecting multi-scale information from cellular to tissue levels. Processing an entire WSI directly is infeasible due to GPU memory constraints; thus, Multiple Instance Learning (MIL) has emerged as the standard solution by partitioning WSIs into tiles. While recent two-stage MIL frameworks partially achieve memory efficiency by decoupling tile-level extraction from slide-level modeling, they still face four limitations: (1) the conflict between training throughput and inference memory efficiency, (2) the high susceptibility to overfitting on small-scale WSI datasets with sparse supervision, (3) the disruption of spatial structural integrity during sampling-based training, and (4) the inadequate modeling of multi-scale feature interactions within long sequences. We therefore introduce PathRWKV, a novel State Space Model designed for efficient and robust WSI analysis. To resolve the computational trade-off, we propose an asymmetric structure utilizing max pooling aggregation, enabling parallelized training for high throughput and recurrent inference with constant ( O (1)) memory complexity. To mitigate overfitting, we employ random sampling to enhance data diversity, with a multi-task learning module to regularize feature learning on limited data. To restore spatial context, we introduce 2D sinusoidal position encoding to perceive the relative locations of tissue tiles. To capture comprehensive representations, we integrate TimeMix and ChannelMix modules, enabling dynamic multi-scale feature modeling across temporal and spatial dimensions. Experiments on 29,073 WSIs across 11 datasets demonstrate that PathRWKV outperforms 11 state-of-the-art methods on 10 datasets, establishing it as a scalable and solution with application potential.

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Chest X-Ray Vision-Language pretraining (VLP) leverages large-scale radiograph-report pairs to develop joint image-text representations, demonstrating significant potential for medical image diagnosis. However, existing VLP approaches often overlook the multi-view nature of chest X-Rays, and some multi-view methods apply uniform feature fusion, neglecting view-key semantic contributions. Moreover, random cross-modal Masked Language Modeling (MLM) fails to facilitate effective interactions, impeding representation alignment. Additionally, global alignment in VLP may lead to the false-negative problem. To address these limitations, we propose a novel medical VLP framework comprising three core components. First, a Key Semantics-enhanced Multi-view MLM module aggregates pathology-relevant patches across views, providing semantically rich supervision for MLM. A local semantics enhancing approach, which identifies and aggregates pathology-relevant key patches across views to guide MLM. Second, a Frontal-Lateral Alignment module extracts view-specific pathological features, ensuring semantic consistency and preserving critical information during aggregation. This module independently extracts pathological features from both views to preserve view-specific information while ensuring semantic consistency, which mitigates the loss of crucial information during aggregation. Third, a High-order Semantic Alignment approach mitigates false-negative issues by aligning features with semantically consistent clusters, enhancing global alignment through prototype-level semantics. Extensive experiments across seven public datasets demonstrate that our framework outperforms state-of-the-art methods in four downstream tasks, validating its efficacy. The code is available at https://github.com/sajiutea/F-L.

胸部X射线视觉语言预训练（VLP）利用大规模放射影像-报告对来开发联合图像-文本表示，在医学图像诊断中展现出巨大潜力。然而，现有的VLP方法通常忽略胸部X射线的多视图特性，且一些多视图方法采用统一的特征融合，忽视了不同视图的关键语义贡献。此外，随机跨模态掩码语言建模（MLM）未能促进有效交互，阻碍了表示对齐。同时，VLP中的全局对齐可能导致假阴性问题。为解决这些局限，我们提出了一种新颖的医学VLP框架，包含三个核心组件。首先，关键语义增强的多视图MLM模块跨视图聚合病理相关图像块，为MLM提供语义丰富的监督。一种局部语义增强方法，识别并聚合跨视图的病理相关关键图像块以指导MLM。其次，前后位对齐模块提取视图特定的病理特征，确保聚合过程中的语义一致性并保留关键信息。该模块独立提取两个视图的病理特征以保留视图特定信息，同时确保语义一致性，从而减轻聚合过程中关键信息的丢失。第三，高阶语义对齐方法通过将特征与语义一致的聚类对齐来缓解假阴性问题，通过原型级语义增强全局对齐。在七个公共数据集上的大量实验表明，我们的框架在四个下游任务中优于最先进的方法，验证了其有效性。代码可在 https://github.com/sajiutea/F-L 获取。

Two-dimensional cell culture models have long been a cornerstone of biomedical research; however, they often fail to accurately replicate the in vivo environment. In recent years, three-dimensional (3D) cell cultures, particularly 3D spheroid models, have gained recognition for their ability to better mimic the complexities of the in vivo environment, making them valuable tools for studying cellular behavior and responses. Tumor spheroids, in particular, have significant applications in anticancer therapy evaluation, providing a more physiologically relevant model by simulating the spatial architecture and microenvironment of tumors. However, due to the limitations imposed by optical diffraction and background noise in 3D imaging, traditional imaging methods are unable to accurately resolve the growth, morphological changes, and drug responses of tumor spheroids. To address this issue, super-resolution imaging technologies have emerged. Structured illumination microscopy (SIM) combined with reconstruction algorithms can effectively enhance resolution, but challenges such as limited light penetration of single-photon imaging and high background noise remain in 3D imaging. In this paper, an advanced SIM technology with large depth and low noise 3D imaging capability is developed. This study introduces a novel frequency-specific denoising method (FSDM) to effectively reduce noise through adjusting the weights of high-frequency signals to preserve image details. The FSDM optimization significantly reduces background interference from deeper tissue layers, improving image details and the overall quality of 3D imaging. For the first time, scanning SIM is integrated with two-photon microscopy (TPEF-SIM) for 3D imaging, leveraging the strengths of both techniques to enhance resolution and overcome light penetration limitations.

二维细胞培养模型长期以来是生物医学研究的基石；然而，它们常常无法精确再现体内环境。近年来，三维（3D）细胞培养，特别是3D球体模型，因能更好地模拟体内环境的复杂性而获得认可，使其成为研究细胞...的有价值工具。

Organoids are three-dimensional (3D) in vitro models for studying tissue development, disease progression, and physiological responses. Holotomography (HT) enables long-term, label-free imaging of live organoids by reconstructing volumetric refractive-index (RI) maps, but quantitative analysis is limited by the missing-cone artifact, which introduces anisotropic resolution and axial distortion. Here, we present a quantitative analysis framework that addresses the missing-cone problem at the level of image representation rather than reconstruction. We introduce morphology-preserving holotomography (MP-HT), a torus-shaped spatial filtering strategy that emphasizes high-spatial-frequency RI texture while suppressing low-frequency components most susceptible to missing-cone-induced distortion. Based on MP-HT, we develop a 3D segmentation pipeline for robust separation of epithelial and luminal structures, together with a model-based RI quantification approach that incorporates the system point spread function to enable morphology-independent estimation of dry-mass density and total dry mass. We apply the framework to long-term imaging of live hepatic organoids undergoing expansion, collapse, and fusion. In representative organoids, the framework provides consistent segmentation across diverse geometries and enables quantitative characterization of epithelial-lumen remodeling, collapse-associated loss of morphometric stability, and transient biophysical fluctuations during fusion. Overall, this work establishes a physically transparent and reproducible approach for quantitative, label-free analysis of organoid dynamics in 3D.

类器官是用于研究组织发育、疾病进展和生理反应的三维体外模型。全息断层成像（HT）通过重建体积折射率（RI）图，能够对活体类器官进行长期、无标记成像，但定量分析受到缺失锥伪影的限制，该伪影引入了各向异性分辨率和轴向畸变。他...

Low-dose computed tomography (CT) denoising is crucial for reduced radiation exposure while ensuring diagnostically acceptable image quality. Despite significant advancements driven by deep learning (DL) in recent years, existing DL-based methods, typically trained on a specific dose level and anatomical region, struggle to handle diverse noise characteristics and anatomical heterogeneity during varied scanning conditions, limiting their generalizability and robustness in clinical scenarios. In this paper, we propose FoundDiff, a foundational diffusion model for unified and generalizable LDCT denoising across various dose levels and anatomical regions. FoundDiff employs a two-stage strategy: (i) dose-anatomy perception and (ii) adaptive denoising. First, we develop a dose- and anatomy-aware contrastive language-image pre-training model (DA-CLIP) to achieve robust dose and anatomy perception by leveraging specialized contrastive learning strategies to learn continuous representations that quantify ordinal dose variations and identify salient anatomical regions. Second, we design a dose-and anatomy-aware diffusion model (DA-Diff) to perform adaptive and generalizable denoising by synergistically integrating the learned dose and anatomy embeddings from DA-CLIP into diffusion process via a novel dose and anatomy conditional block (DACB) based on Mamba. Extensive experiments on a large simulated multi-dose CT dataset spanning three anatomical regions, together with cross-dataset evaluations on Mayo-2016, CQ500, and piglet datasets, demonstrate superior denoising performance and strong generalization to unseen dose levels and anatomical regions. The codes and models are available at https: //github.com/hao1635/FoundDiff.

低剂量计算机断层扫描（CT）去噪对于减少辐射暴露同时确保诊断可接受的图像质量至关重要。尽管近年来深度学习（DL）推动了显著进展，但现有的基于DL的方法通常针对特定剂量水平和解剖区域进行训练，难以处理在...期间出现的多样噪声特性和解剖异质性。

This paper addresses the challenge of multi-disease diagnosis by integrating causal reasoning into the diagnostic framework. In clinical practice, multiple conditions often co-occur, making multi-disease diagnosis more relevant than isolated single-disease cases. However, most deep learning methods focus on single-disease detection and fail to capture the complexity of diagnosing concurrent conditions. Even in multi-label settings, existing approaches mainly rely on correlation-based inference, capturing statistical associations rather than true causal relationships. This can lead to spurious feature-disease associations, where features linked to one disease are mistakenly attributed to another due to frequent co-occurrence, ultimately undermines diagnostic accuracy and interpretability. To address this challenge, we propose a novel framework that incorporates causal intervention into multi-label medical image diagnosis, enabling the model to identify true causal signals rather than misleading correlations arising from co-occurring diseases. Specifically, we model latent disease-related confounders and apply backdoor adjustment to disentangle genuine causal effects from spurious associations. This is achieved by implicitly learning shared feature representations that serve as confounding variables, which are then used to refine image-derived features during prediction. The resulting causal adjustment allows the model to focus on disease-specific cues, improving accuracy and interpretability. Extensive experiments on four diverse medical imaging datasets: ODIR (color fundus photography), LID-FFA (fundus fluorescein angiography), Endo (colonoscopy), and Chestpert (X-ray) demonstrate that our method consistently outperforms existing approaches. Furthermore, our model also effectively separates the diagnosis of co-occurring diseases, high-lighting the potential of causal reasoning to enhance the reliability and clinical applicability of AI-assisted diagnosis. The source code is publicly available at https://github.com/davelailai/BankCausal.git.

本文通过将因果推理融入诊断框架，解决了多疾病诊断的挑战。在临床实践中，多种疾病常常同时发生，使得多疾病诊断比孤立单疾病病例更具相关性。然而，大多数深度学习方法专注于单疾病检测，未能捕捉到诊断并发疾病的复杂性...

Proton Density Fat Fraction (PDFF) is the gold standard for non-invasive fatty liver diagnosis, but its reliance on Magnetic Resonance Imaging (MRI) limits broad clinical applicability. Motivated by the accessibility of B-mode Ultrasound (US) in fatty liver assessment, we propose a novel framework for liver segmentation and PDFF prediction from US images. To enhance generalization ability despite limited paired US-PDFF data, our framework integrates a cross-task self-supervised pretext task that extracts semantic features to guide echo intensity capture, benefiting both liver segmentation and PDFF prediction. To address the noise and artifacts inherent in US images, our framework leverages T2-weighted imaging (T2WI) exclusively during training to establish a feature bridge between US and PDFF, thereby enhancing PDFF prediction. Once trained, the model relies solely on US for inference, making it a practical and cost-effective alternative to MRI-based PDFF estimation. Additionally, our framework introduces an uncertainty-augmented adversarial loss function to refine liver boundary delineation, further improving segmentation and PDFF prediction accuracy. Experimental results demonstrate that our method outperforms state-of-the-art methods in liver segmentation and PDFF prediction; and in a specific application study, our predicted PDFF achieves accuracy comparable to real PDFF for hepatic steatosis classification, highlighting its clinical potential. The full source code and detailed documentation are publicly available at https://github.com/D0ngZhang/SSTB.

质子密度脂肪分数（PDFF）是无创脂肪肝诊断的金标准，但其依赖于磁共振成像（MRI）限制了广泛的临床应用。受B型超声（US）在脂肪肝评估中的可及性启发，我们提出了一种新颖的框架，用于从超声图像中进行肝脏分割和PDFF预测。为了提高泛化能力，尽管...

With the growing application of deep learning (DL) in dental image analysis, numerous datasets and models have been proposed. Periapical radiographs (PR), as one of the most common imaging modalities in clinical dentistry, play a critical role in endodontics. However, due to the high cost of manual annotation and interpretation challenges caused by poor projection and imaging artifacts, publicly available high-quality PR datasets remain scarce, severely limiting the development of DL-based PR analysis models that rely on large-scale annotated data. To address this issue, we introduce PRAD++, a large-scale PR analysis dataset annotated by clinical experts, consisting of 10,000 PR images with multi-level annotations, including 9 pixel-level segmentation categories and 17 image-level classification labels. Building upon PRAD++, we propose PRNet++, an end-to-end PR analysis network. The framework leverages the Multi-scale Wavelet Convolution (MWCN) network and the Channel Fusion Attention (CFA) mechanism to effectively model and integrate multi-scale features. In addition, an Expert Prior Injection (EPI) loss is designed to incorporate domain-specific dental knowledge into the learning process, refining classification predictions based on segmentation outputs to ensure accuracy and clinical interpretability. Extensive experiments on the PRAD++ dataset demonstrate that PRNet++ consistently outperforms state-of-the-art (SOTA) methods, achieving an average DSC of 81.25% for segmentation, alongside macroand micro-averaged PR-AUCs of 66.58% and 79.10% for classification. Significantly surpassing the runner-up, PRNet++ exhibits enhanced robustness and interpretability in clinically challenging categories. Furthermore, comprehensive ablation and visualization analyses validate the efficacy of individual components and the parameter sensitivity of the EPI loss.

随着深度学习在牙科图像分析中的应用日益广泛，已经提出了许多数据集和模型。根尖周X光片（PR）作为临床牙科中最常见的影像学方法之一，在牙髓病学中起着关键作用。然而，由于手动标注成本高以及投影不良和成像伪影导致的解读挑战，公开的...

Medical imaging studies often rely on a single sample per subject, assuming it is representative of their physiological traits. However, variations in how input descriptors are defined or computed (e.g. due to a lack of consensus in the scientific field) may have a crucial impact on the analysis, and are hardly considered in practice. In this paper, we propose an original strategy based on representation learning to estimate a parametric map reflecting the impact of such definitional differences on a given physiological descriptor, previously extracted from medical images. We consider the different definitions or computations of such physiological descriptors as different high-dimensional data, potentially of heterogeneous types. We specifically focus on myocardial deformation (strain), for which there is limited agreement on its definition. We first use manifold alignment to match the latent representations associated with the different definitions of this descriptor. Then, we formulate plausible distributions in the latent space to represent definitional divergence across descriptors, from which we reconstruct a high-dimensional parametric map to visualize such definitional divergence. Due to the lack of proper ground truth for this specific clinical application, we first demonstrate this methodology on toy experiments and then expand the evaluation on right ventricular strain data from subjects obtained from 3D echocardiographic image sequences, for which different types of strain are available at each point of the right ventricle endocardial surface mesh. Beyond this illustrative application, our methodology has the potential to be generalised to many other population analyses considering heterogeneous high-dimensional descriptors.

医学影像研究通常依赖于每个受试者的单个样本，假设其代表其生理特征。然而，输入描述的定义或计算方式的变化（例如由于科学领域缺乏共识）可能对分析产生关键影响，并且在实践中很少被考虑。在本文中，我们提出了一种基于代表性的原始策略...

Automatic echocardiography video segmentation is crucial for accurate diagnosis of cardiovascular diseases, as high-quality segmentation significantly improves automated lesion detection. However, deep learning methods still face challenges including speckle noise, dynamic ventricular changes, limited annotations, and the requirement for real-time inference in clinical practice. In this paper, we propose MSSNet, a novel semi-supervised method based on the efficient sequence modeling architecture Mamba, to address these challenges. To enhance noise robustness and foreground tracking, we design a flexible and efficient spatiotemporal synergistic guidance (SSG) module that leverages attention weights from historical frames to guide subsequent segmentation. By incorporating stable structural context and modeling inter-frame dependencies through weight propagation, SSG effectively mitigates segmentation errors caused by strong local noise and ventricular dynamics while maintaining low computational complexity. To alleviate limited annotation, we further introduce two semi-supervised modules: region-wise adaptive cross-mix (RAC) and dynamic offset correction (DOC). RAC simulates clinically plausible samples via regional mixing to enrich semantic details and strengthen feature learning, while DOC continuously integrates features from high-quality pseudo-labels during training. Experiments on the CAMUS and EchoNet-Dynamic datasets demonstrate that MSSNet outperforms existing SOTA methods in segmentation accuracy and achieves notable improvements in inference speed. The code is available at https://github.com/SSS666-klk/MSSNet.

自动超声心动图视频分割对于心血管疾病的准确诊断至关重要，因为高质量的分割显著提高了自动病灶检测。然而，深度学习方法仍然面临挑战，包括散斑噪声、动态心室变化、有限的标注以及临床实践中实时推理的需求。在本文中，我们……