MBAS2024: A large-scale benchmark for multi-class bi-atrial segmentation in multi-center contrast-enhanced MRIs [0.03%]
MBAS2024：一项大规模基准测试，用于多中心增强MRI中双心房的多类分割任务
Fangqiang Xu,James Kennelly,Alexander M Zolotarev et al.
Fangqiang Xu et al.
Atrial fibrillation (AF), the most common cardiac arrhythmia, affects one in three adults over 45 years of age. Improving its treatment requires a better understanding of bi-atrial anatomy. Existing benchmarks have focused on the left atria...
Respiratory motion augmentation for personalized super-resolution (RMApSR) of 3D cine MR images in MRI-guided radiotherapy [0.03%]
基于MRI引导放射治疗的3D心脏电影MR图像个性化超分辨率重建的呼吸运动增强方法(RMApSR)
Younghun Yoon,Jiwon Sung,Jun Won Kim et al.
Younghun Yoon et al.
The technical implementation of volumetric magnetic resonance imaging (MRI) has the potential to substantially advance tumor tracking in MRI-guided radiotherapy (MRIgRT). However, existing three-dimensional cine MRI techniques remain constr...
Biom3d, a modular framework to host and develop 3D segmentation methods [0.03%]
一种模块化框架 Biom3d：用于托管和开发 3D 分割方法
Guillaume Mougeot,Sami Safarbati,Hervé Alégot et al.
Guillaume Mougeot et al.
Bioimage frameworks based on artificial intelligence (AI) offer powerful tools for image segmentation, but their technical overhead often creates a gap between developers and the broader bioimaging community. Biom3d addresses this challenge...
Embracing intra-class heterogeneity for semi-supervised medical image segmentation: From diversity to precision [0.03%]
拥抱类内异质性以实现半监督医学图像分割：从多样性到精准度
Yuqi Liu,Yufei Chen,Wei Fu et al.
Yuqi Liu et al.
Due to the scarcity of expert-annotated data, Semi-Supervised Medical Image Segmentation (SSMIS) has emerged as a promising approach. Many anatomical structures in medical images exhibit significant intra-class heterogeneity, with different...
Real-time patient-specific microwave ablation zone prediction via a unified bioheat solver and MRI-informed perturbation learning [0.03%]
基于生物热解算器和MRI信息的扰动学习的实时个性化微波消融区域预测方法
Rendong Chen,Qiaowei Du,Fan Xiao et al.
Rendong Chen et al.
Microwave ablation is a crucial option for liver tumors, with success hinging on generating a suitably sized ablation zone for complete tumor eradication. Mathematical modeling supports ablation zone prediction and clinical decision-making,...
Generative morphodynamic forecasting enables robust zero-shot volumetric medical segmentation [0.03%]
生成形态动力学预测实现了稳健的零样本医学体积分割
Duwei Dai,Caixia Dong,Guowei Dai et al.
Duwei Dai et al.
Video foundation models show strong potential for interactive volumetric medical image parsing but can suffer from memory drift when applied directly to 3D medical volumes with severe, patient-specific topological changes. In the evaluated ...
ContiMorph: An unsupervised learning framework for cardiac motion tracking with time-continuous diffeomorphism [0.03%]
基于时间连续微分同胚的心脏运动追踪的无监督学习框架
Mingfeng Jiang,Xiaowei Ruan,Luyan Zheng et al.
Mingfeng Jiang et al.
Cardiac motion tracking is essential for evaluating cardiac function and diagnosing cardiovascular diseases. However, existing tracking methods primarily depend on scaling-and-squaring (SS) integration to derive discrete Lagrangian motion f...
Jiahui Peng,He Yao,Jingwen Li et al.
Jiahui Peng et al.
Contrastive Language-Image Pre-training (CLIP) has demonstrated outstanding performance in global image understanding and zero-shot transfer through large-scale text-image alignment. However, the core of medical image analysis often lies in...
Multi-organ guided diagnosis of mild cognitive impairment via hierarchical alignment and knowledge distillation [0.03%]
基于分层对齐和知识蒸馏的多器官轻度认知障碍引导诊断方法
Shilun Zhao,Fan Li,Kaicong Sun et al.
Shilun Zhao et al.
Mild cognitive impairment (MCI) is widely recognized as a highly heterogeneous and critical prodromal stage of dementia. Emerging evidence reveals that MCI is a systemic condition, with pathological and metabolic alterations manifesting in ...
SUDA: Simultaneous unsupervised knowledge distillation and adaptation of foundation models for efficient pathological image analysis [0.03%]
SUDA：用于高效病理性图像分析的基础模型的同步无监督知识蒸馏和自适应
Lanfeng Zhong,Kun Qian,Weiren Zhao et al.
Lanfeng Zhong et al.
Pathology foundation models have greatly advanced pathological image analysis due to their generalizable feature representation capabilities after learning from a large dataset. However, they still face two critical limitations: first, thei...