Domain Elastic Transform (DET) is a grid‑free, probabilistic framework that jointly aligns geometry and high‑dimensional vector‑valued functions on irregular sparse manifolds, such as those found in spatial transcriptomics. By treating data as functions rather than voxelized images, DET performs unsupervised, scalable registration through sampled point alignment and displacement interpolation, guided by a joint spatial‑functional Bayesian likelihood. Evaluations on MERFISH mouse‑brain slices and Stereo‑seq mouse‑embryo atlases show DET achieving superior spatial overlap and topology compared to existing pipelines, with an accelerated variant delivering high label‑transfer accuracy.
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.
CAPS is a Clinically‑Verifiable Anatomy‑Prioritized Synthesis framework designed to impute missing MRI modalities for brain tumor segmentation. It uses a hybrid loss (L₁ + L_AFP + L_DualDice) to preserve deep anatomical features and topological boundaries, achieving high synthesis fidelity and robust segmentation (82.38% mean Dice) on the BraTS Adult Glioma dataset. The method also generalizes well to unseen cohorts and has been validated by blinded radiologists, who report improved image quality and clinical utility.
arXiv:2608.24025v1 Announce Type: new Abstract: Endpoint-only unsupervised 4D medical image interpolation synthesizes intermediate volumes from sparsely sampled sequences with only the start and end...
arXiv:2607. 23343v1 Announce Type: cross Abstract: Intraoperative 2D/3D registration aligns preoperative CT volumes with intraoperative X-ray or fluoroscopic images and is essential for image-guided interventions.
arXiv:2610.08983v1 Announce Type: new Abstract: Generative inpainting of brain MRI volumes is essential for synthesizing healthy tissue in pathological regions, improving the accuracy and reliability...
arXiv:2609.24560v1 Announce Type: new Abstract: Confocal Laser Endomicroscopy (CLE) provides real-time, cellular-resolution optical biopsy but has a narrow field of view, which image mosaicing can ex...
Multi-parametric MRI is the gold standard for neuro-oncology, yet acquiring complete 4-modality protocols may be unfeasible in real-world clinical practice. When handling missing modalities, existing...
SSFMamba is a Mamba-based framework that fuses spatial and frequency-domain modeling for 3D medical image segmentation. It uses a dual-branch design, with a spatial branch preserving voxel-level textures and a frequency branch capturing global context through spectral components. The core 3D Multi-Directional Scanning Mechanism incorporates Hermitian symmetry and State Space Models to enable direction-aware global modeling, leading to superior performance on MRI and CT datasets, especially for low-contrast organs like the pancreas.
arXiv:2608. 08173v1 Announce Type: cross Abstract: Longitudinal MRI enables sensitive measurement of structural brain change for studying aging and neurodegenerative disease.
arXiv:2507. 04704v3 Announce Type: replace-cross Abstract: Understanding how cellular morphology, gene expression, and spatial context jointly shape tissue function is a central challenge in biology.
The paper presents an anatomy‑aligned surface learning framework for reconstructing patient‑specific 4D myocardial surfaces from sparsely sampled short‑axis cine MRI. By parameterizing epicardial and endocardial surfaces on a shared circumferential‑longitudinal UV domain, the method transforms irregular 3D reconstruction into structured coordinate‑field completion, enabling explicit correspondence across subjects and cardiac phases. Experiments on three public datasets show the approach outperforms mesh‑based and implicit methods, achieving Chamfer distances around 2.6–2.9 mm and preserving ventricular function with small errors in volume and ejection fraction.
The paper introduces a geometry‑guided sampling operator that directs feature sampling rather than altering convolution kernels in 3D encoder‑decoder networks. By predicting local orientations and bounded step sizes, the operator samples symmetrically around each voxel, generating compact geometric and boundary cues that improve fine‑structure segmentation. Replacing stride‑1 and stride‑2 operations in a 3D U‑Net yields consistent gains on BraTS, MSD Hepatic Vessel, and TDSC‑ABUS datasets, with better boundary metrics and fewer parameters, and the operator can be integrated into other backbones without architectural changes.