FU‑Mamba is a new framework for oralscan image segmentation that combines dynamic scanning with frequency‑domain enhancement. It uses a Dynamic Mamba Block to learn adaptive sampling offsets and perform bilinear interpolation, preserving spatial coherence, and a frequency enhancement block that balances spectral components via wavelet‑guided decomposition and spectrum pooling. Experiments show a 1.1% improvement in mean intersection over union on a dental segmentation dataset.
arXiv:2605.30972v2 Announce Type: replace
Abstract: Accurate 3D medical image segmentation requires both fine spatial detail and long-range volumetric context. Although Mamba provides efficient long-...
By Bakht Zada, Chao Tong, Qile Su, Shuai Zhang
arXiv:2608. 15537v1 Announce Type: cross Abstract: Accurate boundary delineation remains a persistent challenge in dermoscopic image segmentation because of blurred lesion margins, heterogeneous textures, and complex background artifacts.
By Wang Jiangtao, Nur Intan Raihana Ruhaiyem, Fu Panpan, Yang Yu, Huang Yan
arXiv:2606. 05998v1 Announce Type: cross Abstract: Oral 3D modelling is one of the most essential stages in dentistry, and many different approaches, such as impression taking and intraoral scanning, are commonly used for this phase, each with notable limitations.
By Jihun Cho, Soo-Yeon Jeong, Eun-Jeong Bae, Sun-Young Ihm
arXiv:2606. 03069v1 Announce Type: cross Abstract: Generalized segmentation of medical images prevents performance degradation when different imaging devices and clinical protocols are used across multiple domains.
By Aqsa Naseer, Maryam Bibi, Syeda Samiya Urooj, Muhammad Khurram Shahzad
The paper presents TLNM, a Mask R‑CNN based system that detects, numbers, and segments teeth in smartphone photographs. It incorporates a masked gray‑world white‑balancing step and an anatomically constrained detection layer to handle patient‑generated variability. Evaluated on internal and external datasets, the model achieved high AP50, PQ, and F1 scores, demonstrating robust performance across diverse populations and imaging conditions.
By Arash Nedaei, Henna Tiensuu, Elina V\"ayrynen, Saujanya Karki, Jaakko Suutala
arXiv:2606. 04493v1 Announce Type: cross Abstract: Correspondence pruning aims to identify inliers from an initial set of correspondences.
By Zhihua Wang, Yanping Li, Yizhang Liu
arXiv:2510. 17529v3 Announce Type: replace-cross Abstract: Active Surveillance (AS) is a treatment option for managing low and intermediate-risk prostate cancer (PCa), aiming to avoid overtreatment while monitoring disease progression through serial MRI and clinical follow-up.
By Yovin Yahathugoda, Davide Prezzi, Patricia A. Gutierrez, Piyalitt Ittichaiwong, Vicky Goh, Sebastien Ourselin, Michela Antonelli
arXiv:2608. 07564v1 Announce Type: cross Abstract: In digital dentistry and oral surgery, the registration of jawbone CT and intraoral scanner (IOS) data is essential for integrating internal bone structure with high-resolution dental surface geometry.
By Sho Mitarai, Hikaru Kayo, Hisashi Ozaki, Yuichiro Imai, Megumi Nakao
arXiv:2510.14383v4 Announce Type: replace
Abstract: Accurate brain tumor segmentation is significant for clinical diagnosis and treatment but remains challenging due to tumor heterogeneity. Mamba-bas...
By Danish Ali, Ajmal Mian, Naveed Akhtar, Ghulam Mubashar Hassan
arXiv:2608.21300v1 Announce Type: new
Abstract: Foundation models for medical image segmentation, like prompt-based MedSAM, generalize well across domains and modalities, often in zero or few-shot se...
By Marko Haralovi\'c, Sounic Akkaraju, Carlo Baretta, Vasil Zapryanov, Alexia Briassouli
The paper introduces FAN-LoRA, a Fourier‑adaptive nonlinear low‑rank adaptor designed to improve domain adaptation of vision foundation models like SAM for medical imaging. By decoupling frequency components into a low‑pass B‑spline branch for global structure and a high‑pass Fourier branch for local texture, FAN‑LoRA addresses performance drops caused by domain shifts. Experiments on three cross‑modality and cross‑center benchmarks show that FAN‑LoRA outperforms existing PEFT methods, achieving higher Dice scores and lower boundary errors while remaining computationally efficient.
By Ziquan Liu, Zhewei Zhu, Xuyang Shi