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
TopoFuse introduces a topology-aware tri-planar fusion method for 3D cryo-electron tomography segmentation. It replaces traditional loss penalties with a differentiable projection operator that identifies and sparsely edits critical voxels to enforce specified topological constraints. The approach achieves a 54% reduction in Betti number error, a 4.6-point Dice improvement, and edits only 3.1% of voxels across three benchmarks.
By Rohit Kumar Salla, Neelesh Gupta, Xingjian Li, Min Xu
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. 10255v1 Announce Type: cross Abstract: Cryo-electron tomography (cryoET) has emerged as a powerful tool in structural and cellular biology by enabling direct visualization of macromolecular structures within intact cells, thereby linking molecular architecture to cellular organization in a native context.
By Jonathan Schwartz, Utz Heinrich Ermel, C. Braxton Owens, Zhuowen Zhao, Ariana Peck, Gus L. W. Hart, Grant J. Jensen, Bridget Carragher, Dari Kimanius
arXiv:2609.09832v1 Announce Type: new
Abstract: Recent advances in high-resolution fluorescence and electron microscopy have enabled nanoscale imaging across increasingly large brain volumes, but the...
By Jiayi Ding, Hu Zhao
arXiv:2608.30768v1 Announce Type: new
Abstract: Automatic neuron reconstruction from light microscopy images is a central problem in computational neuroanatomy. While recent methods have achieved enc...
By Zekang Yang, Jiamin Li, Zhenghua Li, Jiaqi Fan, Zengcai Guo, Xiaolin Hu
The paper introduces Structural Dual Super‑Resolution (SDN), a novel approach that shifts from pixel‑level super‑resolution to topological inference for trabecular bone morphology. By training on 2‑D slices and evaluating on 3‑D morphological metrics, SDN learns to predict invariant microstructures from low‑resolution CT inputs, using bidirectional modeling, a multi‑scale consistency discriminator, and four structural duality constraints. The method achieves SSIM of 0.8 and morphological parameters closely matching synchrotron micro‑CT across six metrics, demonstrating cross‑source generalization and trustworthy inference rather than mere pixel generation.
By Fan Zhang, Yi Zhang, Ling Wang
arXiv:2608.28838v1 Announce Type: cross
Abstract: Cryo-electron microscopy (cryo-EM) determines the structures of proteins and macromolecular assemblies at near-atomic resolution, and the final 3D re...
By Riku Itsuji, Yuanhao Wang, Xingjian Li, Seonghui Min, Hideo Saito, Min Xu
Automatic neuron reconstruction from light microscopy images is a central problem in computational neuroanatomy. While recent methods have achieved encouraging results on local image blocks, it remain...
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
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
The paper introduces Sharp Mode Connectivity (SMC), a new formulation of mode connectivity that optimizes for flatness in the entire local neighborhood of a weight-space path rather than just along the path itself. By applying a sharpness-aware approximation to a minimax objective, SMC produces low-loss curves that remain robust to distribution shifts, achieving significant accuracy gains on CIFAR‑10‑C and outperforming standard mode connectivity on several architectures.
By Alejandro Calatrava, Xu Zhang, Ren Wang