Early Access

Metadata-guided cross-modality 3D MRI synthesis aims to generate target-contrast volumes from source-modality data conditioned on clinically available metadata, which is important for enhancing clinical imaging flexibility. However, existing methods still suffer from two main limitations: 1) They neglect spatial dependencies within volumetric representations, yielding structurally ambiguous featur...

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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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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...

Accurate multi-organ segmentation across heterogeneous medical images is pivotal for real-world surgical navigation. The scarcity of annotation constitutes a well-established consensus in the field, prompting semi-supervised learning to emerge as a prominent solution. However, two critical bottlenecks persist in clinical translation: (1) inter-class feature ambiguity, and (2) high multi-source sam...

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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……

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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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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Ultrasound molecular imaging (USMI) is an imaging approach that utilizes targeted microbubbles (MBs) to highlight biomarkers of disease. While differential targeted enhancement (DTE) is the current state-of-the-art for USMI, its reliance on destructive pulses hinders real-time clinical application. We have developed a neural network-based nondestructive USMI, validated in vivo using a transgenic m...

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Synthesizing high-quality images from low-field MRI holds significant potential. Low-field MRI is cheaper, more accessible, and safer, but suffers from low resolution and poor signal-to-noise ratio. This synthesis process can reduce reliance on costly acquisitions and expand data availability. However, synthesizing high-field MRI still suffers from a clinical fidelity gap. There is a need to prese...

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In recent times, geometric foundation models have demonstrated remarkable performance in depth estimation tasks, benefiting from exposure to large-scale data that enables the learning of intricate geometric structures and spatial dependencies. However, their large parameter sizes and high computational complexity pose significant challenges in meeting the efficiency requirements of downstream surg...

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Generalized category discovery aims to identify known medical categories and unknown new medical categories from unlabeled data by migrating knowledge from labeled datasets containing only known categories, which is crucial for disease understanding and precision medicine. Many methods have been proposed and significantly improved the performance of GCD in medical images. However, most of the exis...

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Orthodontic motion planning plays a crucial role in digital orthodontics by predicting tooth motion sequences to assist dentists in formulating treatment plans efficiently. Most prior work generates the entire intermediate tooth motion sequence given the initial and target tooth alignments. In practice, only the initial alignment of the patient is obtained. However, no existing method can predict ...

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Three-dimensional ultrasound localization microscopy (ULM) enables comprehensive visualization of the vasculature, thereby improving diagnostic reliability. Nevertheless, its clinical translation remains challenging, as the exponential growth in voxel count for full 3D reconstruction imposes heavy computational demands and extensive post-processing time. In this row-column array (RCA)-based 3D in ...

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Automated whole-body lesion segmentation in 18F-FDG PET/CT images marks a pivotal breakthrough in oncological diagnostics, substantially improving the accuracy and efficiency of tumor burden assessment. Manual segmentation is often plagued by significant interobserver variability, underscoring the necessity for automated solutions. The synergistic combination of PET’s exceptional sensitivity for d...

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Whole Slide Images (WSIs) have been widely used in computational pathology (CPath) for various tasks. However, obtaining high-quality annotations remains a major bottleneck. Task-aware unsupervised anomaly detection models offer a promising alternative, as they are trained solely on task-specific normal data and can be adapted to clinically defined objectives, such as cancer detection, depending o...

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Colonoscopy video generation delivers dynamic, information-rich data critical for diagnosing intestinal diseases, particularly in data-scarce scenarios. High-quality video generation demands temporal consistency and precise control over clinical attributes, but faces challenges from irregular intestinal structures, diverse disease representations, and various imaging modalities. To this end, we pr...

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Tagged magnetic resonance imaging (tMRI) is a valuable tool for visualizing and quantifying tissue deformation in vivo. Its use is often hampered, however, by tag fading, long computation times, and the challenge of ensuring diffeomorphic, incompressible motion fields. In this paper, we describe a novel integration of the harmonic phase (HARP) approach to tMRI analysis with an unsupervised deep le...

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The purpose of this work is to demonstrate a robust and clinically validated method for correcting sound speed aberrations in medical ultrasound. We propose a correction method that calculates the focus delays directly from the observed two-way distributed average sound speed. The method beamforms multiple coherence images and selects the sound speed that maximizes the coherence for each image pix...

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Multimodal imaging has become an essential tool in clinical ophthalmology, offering complementary perspectives for disease diagnosis. However, current automated diagnostic approaches often fail to fully exploit the rich, complementary information provided by different imaging modalities. In this paper, to advance automated ophthalmic disease diagnosis through effective multimodal data integration,...

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Gleason grading, the clinical gold standard for prostate cancer assessment, is based on subjective evaluation of glandular architecture, resulting in interobserver variability and limited scalability. This highlights the need for automated grading systems. However, their development is hindered by the scarcity of annotated pathology data. Self-supervised learning (SSL) presents a promising solutio...

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Owing to the prohibitive cost of manual annotation for enormous medical images, self-supervised learning (SSL) has gained substantial attention and shown promise in various medical imaging tasks. Among SSL approaches, contrastive learning has emerged as a prominent one, encouraging models to encode semantic information that remains invariant between different augmented views. However, this invaria...

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