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.
By Adina Scheinfeld, Haotan Zhang, Shang Mu, Rudolf L. M. van Herten, Lucas Stoffl, Ali Erturk, Zhuhao Wu, Johannes C. Paetzold
arXiv:2604.19609v2 Announce Type: replace
Abstract: Transformers have become a common foundation across deep learning, yet 3D scene understanding still relies on specialized backbones with strong dom...
By Kadir Yilmaz, Adrian Kruse, Tristan H\"ofer, Daan de Geus, Bastian Leibe
arXiv:2607.12896v3 Announce Type: replace
Abstract: Medical image segmentation foundation models are expected to generalize across diverse clinical scenarios, yet existing universal methods remain fr...
By Yunzhou Li, Jiesi Hu, Yanwu Yang, Hanyang Peng, Chenfei Ye, Jianfeng Cao, Yixuan Yuan, Ting Ma
arXiv:2608. 16810v1 Announce Type: cross Abstract: Identifying and representing object instances such as cells or nuclei is a common task in microscopy image analysis.
By Ziwen Liu, Martin Weigert
Atelier is a self‑supervised framework that uses a transformer‑based hypernetwork to generate implicit neural representations (INRs) for cryo‑EM maps, enabling efficient, scale‑agnostic, coordinate‑conditioned feature extraction. Trained on 5,439 maps from the Electron Microscopy Data Bank, the pretrained INR provides continuous local feature fields that can be used as auxiliary channels for a 3D nested U‑Net, improving voxel‑level property prediction across eight tasks compared to a volume‑only baseline. The approach demonstrates that amortized INRs can serve as a geometry‑aware primitive for large‑scale cryo‑EM analysis.
By Phillip Lo, Sudarshan Babu, Dari Kimanius, Aly A. Khan
The precise pixel-level localization of 2D material flakes is crucial for high-throughput screening. However, traditional fully supervised methods rely on dense annotations, which are costly and time-consuming, severely limiting the practical deployment of segmentation models.
arXiv:2606. 23964v1 Announce Type: new Abstract: Self-supervised learning in fluorescence microscopy often relies on 2D projections, despite the inherently three-dimensional nature of cells.
By Amirhossein Kardoost, Lion Gleiter, Tingying Peng, Carsten Marr
Pix2Rep-v2 is a self‑supervised learning framework that learns pixel‑ and voxel‑level representations for dense medical imaging tasks, using a redundancy‑reduction objective and equivariance principles to scale to 3D and wide field‑of‑view data. The method is evaluated on four datasets across multiple modalities, tasks, and backbones, demonstrating higher data‑efficiency in few‑shot scenarios and competitive performance, such as a +9.3 Dice point improvement in one‑shot segmentation on the M&Ms‑2 dataset. An in‑context dense prototype approach is also proposed, eliminating the need for downstream training.
By S. Sifaoui, E. Angelini, S. Toupin, T. Pezel, L. Le Folgoc
arXiv:2609.22410v1 Announce Type: new
Abstract: Inferring 3D cellular properties from 2D microscopy is difficult when a reference instrument reports only population statistics rather than labels for...
By Santiago Hern\'andez-Orozco, Hector Zenil
arXiv:2604.10766v5 Announce Type: replace
Abstract: Open-set 3D macromolecule detection in cryogenic electron tomography eliminates the need for target-specific model retraining. However, strict VRAM...
By Ming-Yang Ho, Alberto Bartesaghi
arXiv:2608.30870v1 Announce Type: new
Abstract: Semantic segmentation in 3D Gaussian Splatting (3DGS) is crucial for advancing 3D scene understanding. Existing methods predominantly rely on feature d...
By Kun Cao, Di Wang, Haibin Zhu, Haozhi Huang, Xu Wang, Zheng Shi, Guanghua Yang
PePESeg3D introduces perception priors into a multi‑scale 3D Gaussian segmentation pipeline, integrating monocular depth and mask constraints during geometry reconstruction and dense depth‑color cues with view‑consistent centroid supervision during contrastive feature learning. This dual‑stage approach aligns geometry with semantic structure and compensates for incomplete mask supervision from 2D foundation models. Experiments on SPIn‑NeRF, LERF‑Mask, and NVOS benchmarks show state‑of‑the‑art performance in both multi‑scale segmentation and scene reconstruction.
By Sungjae Choi, Seunghee Koh, Junmo Kim