FPicker is a topology-guided framework for filament tracing in low‑signal Cryo‑EM images. It combines a center‑endpoint representation with an open‑curve evolution module to model non‑cyclic connectivity, overcoming limitations of pixel‑wise segmenters, box‑based detectors, sequential trackers, and traditional active contours. On simulated benchmarks, FPicker improves mean spatio‑angular precision by over 40% and reduces topological gap rates by more than 60% under extreme noise, and it achieves state‑of‑the‑art performance on real EMPIAR data after fine‑tuning.
By Tingyin Zhao, Mingtao Huang, Yuan Shen
FPicker is a topology‑guided framework for filament tracing in low‑signal Cryo‑EM images. It combines a center‑endpoint representation with an open‑curve evolution module to model non‑cyclic connectivity, overcoming limitations of pixel‑wise segmenters, box‑based detectors, sequential trackers, and traditional active contours. On simulated benchmarks, FPicker improves mean spatio‑angular precision by over 40 % and reduces topological gap rates by more than 60 % under extreme noise, and it achieves state‑of‑the‑art performance on real EMPIAR data after fine‑tuning.
arXiv:2610.02201v1 Announce Type: cross
Abstract: High-resolution 3D generation increasingly relies on voxel latents and multi-stage pipelines that first predict active structure and then synthesize...
By Tianjiao Yu, Xinzhuo Li, Yifan Shen, Ying Shen, Kiet A. Nguyen, Adheesh Sunil Juvekar, Ismini Lourentzou
arXiv:2609.37177v1 Announce Type: cross
Abstract: Persistent homology (PH) is a frequently used tool for extracting and preserving topological information from image data, particularly in image segme...
By Alexander H. Berger, Marco Fontana, Daniel Rueckert, Johannes C. Paetzold, Laurin Lux, Ulrich Bauer
arXiv:2607. 05815v1 Announce Type: new Abstract: The Minkowski functionals of a field's excursion sets -- area, boundary measure, and Euler characteristic -- describe its level-set morphology; the Euler characteristic is the cheapest handle on topology.
By Gunner Levi Howe
arXiv:2606. 17566v1 Announce Type: cross Abstract: Video diffusion has quickly grown into a key generative serving workload, yet producing each clip demands many denoising iterations over large spatio-temporal latents, which puts low-latency inference out of reach on a single device.
By Kaijian Wang, Yuanyuan Xu, Fanjiang Ye, Ye Cao, Jingwei Zuo, T. S. Eugene Ng, Yarong Mu, Yuke Wang
arXiv:2607. 17962v1 Announce Type: cross Abstract: TabPFN is a transformer-based foundation model for tabular prediction that performs inference without task-specific training by conditioning on a support set and query inputs.
By James Hu, Mahdi Ghelichi
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.
By Sizhe Wang, Himashi Peiris, Zhaolin Chen
H3DNAS is a hardware‑aware compression framework that operates directly on ONNX computational graphs, eliminating the need for original source code or gradient access. It introduces a Channel Dependency Graph to classify operators and compute a provable compression ceiling, and employs a two‑stage hierarchical search that prunes architectures via L1‑importance channel selection and applies GhostConv mutations to Pareto‑optimal candidates. Applied to 3D point‑cloud models on the ModelNet40 dataset, H3DNAS reduces parameters by up to 65.5% and achieves significant inference speedups with negligible accuracy loss.
By Anchit Mulye, Rhythm Baghel, Sujay Kumar Ingle, Hardik Jain
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:2608.21008v1 Announce Type: new
Abstract: Mobile AR frameworks attach a metric pose prior to every casual phone capture, and turning it into reconstruction-grade poses cheaply on CPU is the ste...
By Nikolaos Kyriazis
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.
By Joselyn Romero Avila, Kyriaki-Margarita Bintsi, Ermias Habte, Julia F. Lehman, Suzanne N. Haber, Anastasia Yendiki