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arXiv:2606. 26898v1 Announce Type: cross Abstract: Diffusion MRI (dMRI) tractography enables non-invasive reconstruction of white-matter pathways, but its accuracy is fundamentally limited by indirect, low-resolution measurements of axonal organization.
The paper presents a 3D foundation model for light sheet fluorescence microscopy (LSM) that is pretrained on a large curated set of 3D images from various organisms, stains, and imaging protocols. By jointly optimizing for masked reconstruction and image‑text alignment, the model learns transferable volumetric representations that dramatically reduce the need for annotated data. The pretrained backbone enables efficient few‑shot adaptation to downstream tasks such as segmentation, classification, and deblurring, consistently outperforming baselines according to standard metrics and expert evaluation.
arXiv:2603.26509v2 Announce Type: replace Abstract: Computed tomography (CT) provides rich 3D anatomical detail but is often constrained by high radiation exposure, substantial costs, and limited ava...
arXiv:2608. 07575v1 Announce Type: cross Abstract: Confocal microscopy of optically cleared and swelled tissue resolves complex biological structures in 3D, but such acquisitions are highly anisotropic: along the under-sampled axial direction the structure can appear discontinuous, hampering reconstruction and automated quantitative analysis.
arXiv:2606. 15457v1 Announce Type: cross Abstract: 3D FLAIR MRI is widely recommended as one of the standard MRI sequences for brain imaging in multiple sclerosis (MS), but publicly available MS datasets remain relatively small and vary across scanners, acquisition protocols, and lesion patterns.
The paper investigates fiber bundle segmentation in macaque tracer histology, comparing traditional BCE‑Dice loss with topology‑aware losses such as clDice, Betti matching, and Topograph using a frozen DINOv3 backbone. While BCE‑Dice yields the highest Dice score, Topograph achieves comparable Dice with lower topological error and fewer false positives. The authors also introduce Excess32, a spatial diagnostic that reveals oversegmentation issues not captured by conventional detection metrics, demonstrating that detection metrics alone are insufficient for evaluating segmentation quality.