Early Access

High-fidelity reconstruction of the Posterior Eyeball Shape (PES) is crucial for early diagnosis and timely intervention of sight-threatening diseases such as high myopia, diabetic retinopathy, and glaucoma. However, existing magnetic resonance imaging (MRI)- and optical coherence tomography (OCT)-based methods either provide only coarse scleral geometry or suffer from suboptimal PES representations due to limited field of view (FOV) and detail loss, hindering accurate assessment of intact retinal pigment epithelium (RPE) abnormalities. In this study, we propose the Polar Subarea-Aware Fusion Net (PSAFNet), a novel end-to-end framework that reconstructs complete and high-fidelity PES directly from a single local OCT scan, even under clinically common settings with only 6.25% FOV. To avoid information loss, we reformulate PES reconstruction as a 2D dense regression task and introduce the Ocular Shape Map (OSM), an innovative lossless 2D representation that encodes 3D coordinate attributes into corresponding image channels. PSAFNet then leverages three dedicated modules—Subarea Feature Embedding Module (SFEM), Channel- and Patch-wise Fusion Blocks (CFB/PFB), and Reassemble and Up-sample Module (RUM)—to enhance positional awareness, integrate local–global features, and achieve high-resolution OSM prediction. Furthermore, we construct two large-scale datasets, POSDiag and PESGen, comprising 794 ultra-widefield OCT scans from diverse health conditions and imaging devices, providing a comprehensive benchmark for PES reconstruction. Extensive experiments demonstrate that PSAFNet consistently outperforms existing methods (e.g., EMD=5.58, AAL=97.3%) and exhibits strong clinical relevance, validated by superior performance in downstream disease classification and ophthalmologist evaluations (Expert-Score=82.78%). The source code of the proposed PSAFNet is released at https://github.com/HKUZJ77/PSAFNet.

后眼球形状（PES）的高保真重建对于早期诊断和及时干预高度近视、糖尿病视网膜病变和青光眼等威胁视力的疾病至关重要。然而，现有的基于磁共振成像（MRI）和光学相干断层扫描（OCT）的方法要么仅提供粗略的巩膜几何形状，要么存在PES表示欠佳的问题。

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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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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Accurate MRI-based brain tumor analysis requires not only tumor subtype classification but also localization at an anatomical granularity that is consistent with radiology reports. Most vision-only methods address localization and classification as separate label-prediction tasks, and therefore provide limited alignment with the fine-grained anatomical semantics used in routine reporting. To addre...

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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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Medical image reconstruction from undersampled acquisitions is an ill-posed inverse problem requiring accurate recovery of anatomical structures from incomplete measurements. Physics-driven (PD) network models have gained prominence for this task by integrating data-consistency mechanisms with learned priors, enabling improved performance over purely data-driven approaches. However, reconstruction...

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Sparse-view computed tomography (CT) effectively reduces radiation exposure, yet it degrades image signal-to-noise ratio (SNR) and compromises the reliability of clinical diagnosis. Deep unrolling networks, which integrate the merits of optimization-based and data-driven paradigms, have achieved promising performance for sparse-view CT reconstruction. However, existing learned data consistency (DC...

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Pretrained segmentation models for cardiac magnetic resonance imaging (MRI) often fail to generalize across imaging sequences due to substantial contrast variations. These variations arise from different imaging protocols, yet fundamentally, all contrasts are governed by the same underlying tissue properties, primarily captured by three components: the magnetization strength (M0), T1, and T2. Buil...

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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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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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Few-shot Medical Image Segmentation (FS-MIS) has garnered increasing attention for its ability to reduce reliance on large-scale pixel-wise annotations. How ever, most existing methods rely solely on single-level encoder outputs, neglecting the complementary roles of hi erarchical features in contour refinement and regional dis crimination. This limitation often results in boundary ambi guity and ...

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Accurate T-staging is crucial for guiding personalized treatment strategies for laryngopharyngeal cancer. However, current clinical practice relies on invasive biopsy procedures, whereas CT-based staging remains challenging due to the complex patterns of tumor invasion. Recent computer-aided approaches face two key challenges: 1) Structural relationship modeling: existing methods underrepresent an...

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Skull stripping is a critical preprocessing step for reliable neuroimaging analysis. Although recent deep learning methods have made remarkable progress in accurate brain extraction, their generalization capability remains limited across heterogeneous imaging protocols and diverse pathological conditions. This limitation arises from their predominant reliance on voxel-intensity information without...

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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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Due to their high resolution, pathology images incur substantial storage costs. Although most vendor formats have already adopted lossy compression (typically JPEG) to process such images, the storage requirements remain considerable. However, achieving further reductions in storage requirements while ensuring truly loss-less recompression of already lossy-compressed JPEG images remains a critical...

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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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Automated report generation is limited to static, single-image analysis, failing to address the critical clinical need for longitudinal comparison in monitoring disease progression and treatment efficacy. To bridge this gap, we introduce the new task of Change Radiology Report Generation (CRRG) which aims to automatically generate a comparative radiology report describing interval changes between ...

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Video-based surgical action-triplet recognition represents each surgical action as an ⟨instrument, verb, target⟩ triplet, which is fundamental for understanding complex surgical scenes. However, this task remains challenging due to heterogeneous spatio-temporal characteristics and complex inter-component dependencies. To address these issues, we propose a Relation-Aware Class Activation-guided Mix...

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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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Medical decision systems increasingly rely on data from multiple sources to ensure reliable and unbiased diagnosis. However, existing multimodal learning models fail to achieve this goal because they often overlook two critical challenges. First, various data modalities may learn unevenly, thereby converging to a model biased towards certain modalities. Second, the model may emphasize learning on ...

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Surgical full scene segmentation is essential for laparoscopic assistance but remains challenging due to the high visual similarity among anatomical structures, and illumination variations caused by single moving light source. Moreover, accurately segmenting thin, elongated instruments is still difficult, especially when they appear at oblique orientations. Although Mamba-based segmentation method...

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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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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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