Volume 45, Issue 9

Semi-supervised learning (SSL) offers a promising solution to reduce annotation costs in medical image segmentation. Recent text-enhanced SSL methods incorporate domain-specific textual cues to improve representation learning. However, they often overemphasize language priors and neglect the importance of visual features for precise segmentation. In this work, we propose Retrieval-Augmented Repres...

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Multiple instance learning (MIL) has emerged as the dominant paradigm for whole slide image (WSI) analysis in computational pathology, achieving strong diagnostic performance through patch-level feature aggregation. However, existing MIL methods face critical limitations: (1) they rely on attention mechanisms that lack causal interpretability—the ability to explain why predictions vary across demo...

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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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Unsupervised anomaly detection (UAD) in brain MRI is crucial for early diagnosis, yet generalizing existing methods across diverse diseases, sequences, and missing data scenarios remains a significant challenge. Current reconstruction-based methods often fail to detect subtle anomalies, while conventional translation methods lack flexibility regarding input sequences. To address these limitations,...

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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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Ordinal regression is well-known for lever-aging the underlying inherent order between successive categories to obtain additional regularization beyond traditional probabilistic classification mechanism. However, there are challenges in real-world medical grading tasks: 1) The uneven distribution of disease severity levels, characterized by a long-tailed format, complicates the ordinal regression ...

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Linking circuit level activity to large scale functional organization requires imaging methods combining high spatial resolution, broad coverage, and single trial sensitivity. We present volumetric functional ultrasound imaging (3D-fUS) in behaving macaques, enabling imaging of ~1 cm³ cortical volumes at high spatiotemporal resolution (100 × 150 × 150 μm³ voxels, 1.67 Hz). Visually evoked response...

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Universal medical image segmentation aims to unify heterogeneous datasets or annotation protocols within a single adaptable framework. However, existing prompt-based universal models often overlook background context, neglect hierarchical task dependencies, and struggle to generalize to unseen annotation granularities. These challenges are particularly pronounced in OCT-based retinal layer segment...

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Multi-style virtual staining transforms histological images into multiple staining modalities, offering significant clinical value at reduced cost and time. However, a critical challenge impeding clinical adoption is incompletely paired training data—an inevitable consequence of tissue degradation and processing artifacts during sequential staining. Current methods assume perfectly paired datasets...

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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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X-ray scatter has been a serious concern in computed tomography (CT), leading to image artifacts and distortion of CT values. The linear Boltzmann transport equation (LBTE) is recognized as a fast and accurate approach for scatter estimation. However, for multi-spectral CT, it is cumbersome to compute multiple scattering components for different spectra separately when applying LBTE-based scatter ...

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Neuron segmentation in complex mouse brain images improves neuron reconstruction and supports studies of brain structure and function, while the existing deep learning-based methods do not sufficiently exploit prior information, including neuronal morphology and imaging mechanism. We propose NUNet-LLM, the first LLM-integrated framework for neuron segmentation and reconstruction. NUNet-LLM consist...

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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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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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Medical phrase grounding (MPG) maps textual descriptions of radiological findings to corresponding image regions. These grounded reports are easier to interpret, especially for non-experts. Existing MPG systems mostly follow the referring expression comprehension (REC) paradigm and return exactly one bounding box per phrase. Real reports often violate this assumption. They contain multi-region fin...

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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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Reconstructing dynamic surgical scenes from endoscopic videos remains a fundamental challenge in robot-assisted surgery. Existing methods primarily focus on deformable tissues, overlooking the presence of articulated instruments. To bridge this gap, we present EndoLRMGS, the first unified framework capable of reconstructing both deformable tissue and articulated instruments in a modular approach f...

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Volume electron microscopy (vEM) has revolutionized the nanoscale reconstruction of synapses in neural circuits. However, large-scale vEM techniques relying on serial sectioning suffer from severe anisotropy, where axial resolution is far worse than lateral resolution. This anisotropic imaging induces discontinuities in biological architectures across 3D space, compromising reconstruction accuracy...

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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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