arXiv:2607. 16065v1 Announce Type: cross Abstract: Retinal layer segmentation in Optical Coherence Tomography (OCT) is a fundamental step for extracting quantitative biomarkers of retinal structure.
arXiv:2606. 16234v1 Announce Type: cross Abstract: Fundus fluorescein angiography (FFA) is critical for assessing retinal vascular abnormalities, but its acquisition is invasive and not always feasible.
arXiv:2607. 29337v1 Announce Type: cross Abstract: Background and Objective: Generating realistic medical images with anatomically accurate segmentation masks helps address the shortage of annotated data in medical imaging, particularly in optical coherence tomography (OCT) of mouse eyes, where manual retinal layer delineation is labour-intensive due to tiny structures and required expertise, resulting in scarce datasets.
This study presents four deep‑learning pipelines—two‑dimensional and three‑dimensional—for segmenting age‑related macular degeneration (AMD) and diabetic macular edema (DME) lesions in optical coherence tomography (OCT) images. The models achieve Dice scores between 0.76 and 0.82 and demonstrate strong volumetric and surface calibration (r_vol, r_surf ≥ 0.97) on an in‑domain validation set. Generalization was assessed on the OLIVES clinical cohort using proxy metrics such as biomarker AUROC, central subfield thickness correlation, and longitudinal concordance, showing that the predictions still track clinical biomarkers outside the training distribution, albeit with reduced strength.
arXiv:2609. 20562v1 Announce Type: new Abstract: Automated quality assessment, enhancement, and segmentation of multiple structures in $0.
arXiv:2608. 09636v1 Announce Type: cross Abstract: Accurate 3D neuron segmentation in fluorescence microscopy is critical for neuroscience.
The paper introduces MR‑DiffuSR, a 3‑D latent diffusion framework that uses high‑resolution T1w structural priors to guide super‑resolution of thick‑slice FLAIR MRI scans. By applying cross‑modality structural swin attention and a mixed‑scale degradation strategy, the method avoids hallucinations and remains robust across varying slice thicknesses. On ADNI datasets, MR‑DiffuSR outperforms CNN and 2‑D diffusion baselines, achieving high PSNR, SSIM, and low LPIPS, and maintains strong white‑matter hyperintensity segmentation performance even at 7 mm equivalent slice thickness.
The growing number of medical vision foundation models highlights the need for effective model selection. However, mainstream selection methods rely on exhaustive fine-tuning, which is computationally expensive.
The paper introduces FreNet, a feature reconfiguration framework that incorporates visual priors for medical lesion segmentation. FreNet performs pixel‑level reconfiguration before encoding using an Implicit Prior Neural Network (IPNN) that leverages SAM, and feature‑level reconfiguration during encoding via a Dual‑domain Feature Reconfiguration (DFR) module, which includes a Frequency Decoupling Module (FDM) and a Spatial Localization Module (SLM). Experiments on nine benchmarks across three imaging modalities show that FreNet outperforms state‑of‑the‑art methods, achieving a 5.0% Dice improvement over the best baseline on the ETIS dataset and a 7.2% improvement over SAM.
arXiv:2607. 23371v1 Announce Type: cross Abstract: Vascular segmentation is a standard procedure for clinical diagnosis, yet the specific visual features determining model decisions remain poorly understood.
arXiv:2609.31573v1 Announce Type: new Abstract: Biomedical image segmentation is central to medical image analysis, but practical deployment often faces limited annotations, memory constraints, and c...
arXiv:2607. 05317v2 Announce Type: replace-cross Abstract: Automated detection of intracranial aneurysms (IAs) from CT angiography (CTA) is severely hindered by high false-positive rates.
The study introduces an explainable OCTA pipeline for Alzheimer’s disease phenotyping that combines vessel segmentation, layer‑specific biomarker extraction, and label‑free phenotyping with large language model (LLM) reporting. Using 117 images from 39 subjects, the segmentation models achieved high ROC‑AUC (0.916–0.970) and Dice scores (0.695–0.781), and six vascular biomarkers were used to create subject‑level profiles for exploratory clustering. LLMs (GPT, Gemini, Llama) produced measurement‑grounded reports evaluated for citation faithfulness and diagnostic caution, offering a transparent, non‑diagnostic link between retinal vascular data and Alzheimer’s research.