While three-dimensional imaging is essential for clinical diagnosis, its high cost and long wait times have motivated the use of image-to-3D foundation models to infer volume from two-dimensional modalities. However, because these models are trained on natural images, their learned geometric priors struggle to transfer to inherently planar medical data. A benchmark of five state-of-the-art models (SAM3D, Hunyuan3D-2.1, Direct3D, Hi3DGen, and TripoSG) across six medical and two natural datasets revealed that voxel-based overlap remains uniformly low across all methods due to severe depth ambiguity from single-slice inputs. Despite this fundamental volumetric failure, global distance metrics indicate that SAM3D best captures topological similarity to ground-truth medical shapes, whereas alternative models are prone to oversimplification. Ultimately, these findings quantify the limits of zero-shot single-slice 3D inference, highlighting that reliable medical 3D reconstruction requires domain-specific adaptation and anatomical constraints to overcome complex medical geometries.
Comparative Analysis of GAN and Diffusion for MRI-to-CT translation
Department of Computer Science, University of Copenhagen, Copenhagen, Denmark（哥本哈根大学计算机科学系）；Department of Oncology, Rigshospitalet, Copenhagen, Denmark（哥本哈根医院肿瘤科）
Voxel-based 3D Facies Segmentation from Seismic Data: A Comparative Study
FPT Software AI Center（FPT软件人工智能中心）；Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA)（信息与随机系统研究所）；Université Bretagne Sud（南布列塔尼大学）；University of Liverpool（利物浦大学）
Towards Voxel Spacing Consistency for Medical Image Segmentation
Institute of Medical Robotics, Shanghai Jiao Tong University（上海交通大学医疗机器人研究院）；Department of Ultrasound, Shanghai Chest Hospital（上海市胸科医院超声科）；CAMPAR, Technical University of Munich（慕尼黑工业大学CAMPAR）
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）；Digital Petroleum LLC（数字石油有限责任公司）；Gazprom Neft Science & Technology Center（俄罗斯天然气工业石油公司科技中心）
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；School of Biomedical Engineering, University of Science and Technology of China（中国科学技术大学生物医学工程学院）
针对 SAM 难以建模三维医学切片上下文且全体积提示成本高的问题，本文提出 Slide-SAM，利用三切片滑动窗口和相邻掩码传播提示，实现极少提示下的高效三维分割。
BEV-SAN: Accurate BEV 3D Object Detection via Slice Attention Networks
Peking University（北京大学）；The Chinese University of Hong Kong（香港中文大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；The University of Sydney（悉尼大学）；Beijing University of Posts and Telecommunications（北京邮电大学）
ContiLNN: Mitigating Slice Sampling Discontinuity with Liquid Neural Networks for Medical Image Restoration
Faculty of Information Engineering and Automation, Kunming University of Science and Technology（昆明理工大学信息工程与自动化学院）；Academy of Artificial Intelligence and Advanced Technology, Xi’an Jiaotong-Liverpool University（西交利物浦大学人工智能与先进技术研究院）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
SliceVision-F2I: A Synthetic Feature-to-Image Dataset for Visual Pattern Representation on Network Slices
Department of Electronics and Communication Engineering（电子与通信工程系）；Hajee Mohammad Danesh Science and Technology University（海杰穆罕默德丹什科学与技术大学）；Department of Computer Science and Engineering（计算机科学与工程系）
Plug-and-Play Volumetric Reconstruction for Compressive Sensing Light-Sheet Microscopy
School of Data and Information Sciences, The University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校数据与信息科学学院）；Department of Mathematics, The University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校数学系）；Department of Bioengineering, The University of Texas at Dallas（德克萨斯大学达拉斯分校生物工程系）
