Early Access

The Segment Anything Model (SAM) has demonstrated groundbreaking performance in natural image segmentation, yet its direct application to medical imaging remains suboptimal due to domain shifts in data distributions and the inherent 3D nature of medical data. Although recent SAM-based methods have employed parameter-efficient transfer learning (PETL) to adapt SAM for medical tasks, they often over...

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Simultaneous dual-tracer PET provides more comprehensive information for clinical diagnosis than standard PET imaging, but separating the hybrid dual-tracer signal remains challenging. Deep learning (DL) offers a promising solution. However, most DL methods rely on large datasets with spatiotemporal alignment between dual-tracer and two single-tracer scans. Precise alignment across different scans...

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Photoacoustic computed tomography (PACT) is an emerging imaging modality that combines the molecular contrast of optical absorption with the high spatial resolution of ultrasound detection. Utilizing ultrasound transducers with larger surface areas can improve detection sensitivity. However, when analytic reconstruction methods that neglect the spatial impulse responses (SIRs) of the transducer ar...

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birefringence, retardance, Jones matrix, optic axis orientation, polarization-sensitive optical coherence tomography, retina.Polarization-sensitive optical coherence tomography (PS-OCT) extends OCT by analyzing the polarization states of backscattered light to quantify tissue birefringence. However, conventional implementations require polarization-diverse detection and are therefore incompatible ...

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Automatic medical image segmentation, as a prerequisite for clinical quantitative analysis, forms the basis of computer-aided diagnosis. However, blurry object boundaries caused by factors such as imaging quality and inherent physiological properties of tissues or lesions are the main causes of imprecise segmentation. This aligns with the common understanding that high uncertainty and misclassific...

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Medical image segmentation using deep learning (DL) has enabled the development of automated analysis pipelines for large-scale population studies. However, state-of-the-art DL methods are prone to hallucinations, which can result in anatomically implausible segmentations. With manual correction impractical at scale, automated quality control (QC) techniques have to address the challenge. While pr...

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Vision Transformer has achieved significant performance improvements in natural image segmentation tasks owing to its superior global modeling capabilities. However, applying vision Transformers to 3D medical image segmentation is challenging because of the quadratic computational complexity of the self-attention mechanism and their limited generalization on small-scale datasets. To address these ...

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In medical image analysis, it is common to collect multiple annotations from different clinical experts or raters to mitigate potential diagnostic inaccuracies. However, in computer vision research, it has been conventional to adopt ground-truth labels generated either by majority voting or by selecting a single rater’s annotation. Such practices overlook the rich information embedded in the origi...

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Lymph node (LN) assessment is an essential task in the routine radiology workflow, providing valuable insights for cancer staging and treatment planning. Identifying scatteredly-distributed and low-contrast LNs in 3D CT scans is highly challenging, even for experienced clinicians. Previous lesion and LN detection methods demonstrate the effectiveness of 2.5D approaches (i.e., using 2D backbone with multi-slice inputs), leveraging pretrained 2D model weights and showing improved accuracy as compared to separate 2D or 3D detectors. However, slice-based 2.5D detectors do not explicitly model inter-slice consistency for LN as a 3D object, requiring heuristic post-merging steps to generate final 3D LN instances, which can involve tuning a set of parameters for each dataset. In this work, we formulate 3D LN detection as a slice-by-slice tracking task along the z-axis and propose LN-Tracker, a novel LN tracking transformer, for joint end-to-end detection and 3D instance association. Built upon a DETR-based detector, LN-Tracker decouples transformer queries into distinct track and detection groups with independent matching, enabling comprehensive LN detection while maintaining trajectory consistency. A masked attention mechanism further separates learning between these query groups, and a similarity loss promotes robust interslice LN association, particularly in low-contrast scenarios. Extensive evaluation on four LN datasets shows LN-Tracker’s superior performance, with at least 2.49% gain in average sensitivity when compared to top 3D/2.5D/tracking detectors. Further validation on public lung nodule and prostate tumor detection tasks confirms the generaliz-ability of LN-Tracker as it achieves top performance on both tasks. Code is available at https://github.com/alibaba-damo-academy/LN-Tracker.

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High-field magnetic resonance imaging (MRI) suffers from pronounced magnetic field inhomogeneities and subject-specific field variations, motivating the inscanner design of individually tailored excitation pulses to exploit the full capabilities of high-field MRI. Contemporary methods may employ piecewise-constant (PWC) waveform parameterizations to design excitation pulses, which require many opt...

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Chest X-ray (CXR) plays a pivotal role in clinical diagnosis, and a variety of task-specific and foundation models have been developed for CXR interpretation. However, these models often struggle to adapt to new diagnostic tasks and complex reasoning scenarios. Recently, LLM-based agents have emerged as a promising paradigm for CXR analysis, enhancing model’s capability via tool coordination, mult...

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Ultra-low field magnetic resonance imaging (ULF-MRI) systems operating in open environments are highly susceptible to composite electromagnetic interference (EMI). Different imaging channels respond non-uniformly to EMI owing to their distinct coupling characteristics. Here, we investigate channel-specific interference pathways in a permanent–magnet–based low-field MRI system and show that saddle ...

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Automatic radiology report generation has gained increasing attention for its potential to assist in clinical reporting and reduce the workload of radiologists. Existing 3D radiology report generation methods employ multi-modal foundation model to encode volume-text inputs and produce diagnosis reports, while they ignore the characteristics of 3D volumes including much background regions and suffe...

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Echocardiogram video synthesis has emerged as a promising solution to alleviate data scarcity for training intelligent diagnostic models and to enhance clinical education. However, existing methods are typically conditioned on a single scalar metric, such as left ventricular ejection fraction (LVEF), which fails to capture the complex temporal dynamics of cardiac motion and thus limits clinical ap...

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The ability to learn sequentially from different data sites is crucial for a deep network in solving practical medical image diagnosis problems due to privacy restrictions and storage limitations. However, adapting to the incoming site leads to catastrophic forgetting on past sites and decreases generalizability on unseen sites. Existing Continual Learning (CL) and Domain Generalization (DG) metho...

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Quantitative imaging is an important feature of spectral X-ray and CT systems, especially photon-counting CT (PCCT) imaging systems, which is achieved through material decomposition (MD) using spectral measurements. In this work, we present a novel framework that makes the PCCT imaging chain end-to-end differentiable (differentiable PCCT), with which we can leverage quantitative information in the...

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Brain network analysis based on functional magnetic resonance imaging (fMRI) is crucial for the diagnosis of neurological disorders. Recently, Transformers have been adopted for brain network analysis to mitigate the over-smoothing issue in GNNs. However, they often fail to account for complex topological properties of brain networks and tend to rely on a limited set of regions of interest (ROIs) ...

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We propose a Geometric Energy Field (GEF) supervision framework to enhance the robustness and structural consistency of retinal artery/vein (A/V) segmentation. We introduce two geometrically complementary energy fields: the Distance Energy Field (DEF), which encodes soft, vessel-type-specific spatial territories by modeling pixel-wise proximity to arteries and veins, thereby explicitly capturing t...

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Accurately assessing the severity of pediatric pneumonia using AI-based lung ultrasound (LUS) video scoring remains challenging due to three main limitations: (1) limited availability of expert annotations, (2) underutilization of temporal dynamics, and (3) lack of mechanisms to capture the directional patterns and temporal evolution of diagnostic artifacts. To address these challenges, we introdu...

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Iodine plays a central role in thyroid physiology, and knowledge of its spatial distribution may aid diagnostic framing and follow-up of neoplastic thyroid pathologies. Conventional histology is the gold standard for thyroid assessment but consumes biological material, limiting volumetric context and further analyses, and offers limited specificity for iodine. We present a virtual histology (VH) w...

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Generalizable dense feature matching in endoscopic images is crucial for robot-assisted tasks, including 3D reconstruction, navigation, and surgical scene understanding. Yet, it remains challenging due to difficult visual conditions (e.g., weak textures, large viewpoint variations) and scarce annotated data. To address these challenges, we propose EndoMatcher, a generalizable endoscopic image matc...

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Contrast-enhanced CT (CECT) is essential for clinical evaluation of vessel structures and function. However, high contrast agent dose increases the risk of renal injury. Reducing contrast agent dose decreases the contrast between vessels and surrounding tissues, which complicates diagnosis. Despite their potential in CECT synthesis, existing methods often suffer from edge unclarity, contrast anoma...

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Automatic tooth segmentation is essential for computer-aided diagnosis and treatment planning in dentistry. Proposal generation plays a pivotal role in accurately delineating instance boundaries and is critical for improving segmentation accuracy. However, existing methods suffer from dispersive offset bias, spatial information loss, and spillover-induced centroid shift, all of which are difficult...

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Objective evaluation of quantitative-imaging (QI) methods based on how reliably they measure true values is important for clinical translation. Performing such evaluation with patient data is highly desirable but hindered by the lack of gold standards. To address this challenge, advancing on previous studies, we propose a no-gold-standard evaluation technique, NGSE-Corr, that objectively evaluates...

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Virtual coronary intervention planning (VCIP) aims to optimize the hemodynamic outcomes of percutaneous coronary intervention (PCI) in patients with coronary stenosis. However, its clinical adoption remains constrained by the computational burden associated with evaluating numerous combinatorial intervention strategies, leading to time-consuming workflows and potentially suboptimal decisions in th...

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