Fréchet radiomic distance (FRD): A versatile metric for comparing medical imaging datasets [0.03%]
弗雷歇放射组学距离（FRD）：一种比较医学影像数据集的通用度量标准
Nicholas Konz,Richard Osuala,Preeti Verma et al.
Nicholas Konz et al.
Determining whether two sets of images belong to the same or different distributions or domains is a crucial task in modern medical image analysis and deep learning; for example, to evaluate the output quality of image generative models. Cu...
Generating synthetic MRI scans for improving Alzheimer's disease diagnosis [0.03%]
生成用于改善阿尔茨海默病诊断的合成MRI图像
Rosanna Turrisi,Giuseppe Patané
Rosanna Turrisi
Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the leading cause of dementia. Magnetic Resonance Imaging (MRI) combined with Machine Learning (ML) enables early diagnosis, but ML models often underperform when trai...
Marianne Rakic,Andrew Hoopes,Mazdak S Abulnaga et al.
Marianne Rakic et al.
Deformable templates, or atlases, are images that represent a prototypical anatomy for a population, and are often enhanced with probabilistic anatomical label maps. They are commonly used in medical image analysis for population studies an...
Knowledge-guided multi-geometric window transformer for cardiac cine MRI reconstruction [0.03%]
知识引导的多几何窗口变压器心脏电影MRI重建方法
Jun Lyu,Guangming Wang,Yunqi Wang et al.
Jun Lyu et al.
Magnetic resonance imaging (MRI) plays a crucial role in clinical diagnosis, yet traditional MR image acquisition often requires a prolonged duration, potentially causing patient discomfort and image artifacts. Faster and more accurate imag...
IUGC: A benchmark of landmark detection in end-to-end intrapartum ultrasound biometry [0.03%]
IUGC：端到端产时超声生物测量标志检测的基准测试
Jieyun Bai,Yitong Tang,Xiao Liu et al.
Jieyun Bai et al.
Accurate intrapartum biometry plays a crucial role in monitoring labor progression and preventing complications. However, its clinical application is limited by challenges such as the difficulty in identifying anatomical landmarks and the v...
Hippocampal surface morphological variation-based genome-wide association analysis network for biomarker detection of Alzheimer's disease [0.03%]
基于海马体形态变异的全基因组阿尔茨海默病生物标志物检测关联分析网络
Xiumei Chen,Xinyue Zhang,Wei Xiong et al.
Xiumei Chen et al.
Performing genome-wide association analysis (GWAS) between hippocampus and whole-genome data can facilitate disease-related biomarker detection of Alzheimer's disease (AD). However, most existing studies have prioritized hippocampal volume ...
VHU-Net: Variational hadamard U-Net for body MRI bias field correction [0.03%]
变种Hadamard-U-Net在体MRI偏置场校正中的应用
Xin Zhu,Ahmet Enis Cetin,Gorkem Durak et al.
Xin Zhu et al.
Bias field artifacts in magnetic resonance imaging (MRI) scans introduce spatially smooth intensity inhomogeneities that degrade image quality and hinder downstream analysis. To address this challenge, we propose a novel variational Hadamar...
CATERPillar: a flexible framework for generating white matter numerical substrates with incorporated glial cells [0.03%]
caterpillar：一种生成包含胶质细胞的白质数值底物的灵活框架
Jasmine Nguyen-Duc,Malte Brammerloh,Melina Cherchali et al.
Jasmine Nguyen-Duc et al.
Monte Carlo diffusion simulations in numerical substrates are valuable for exploring the sensitivity and specificity of the diffusion MRI (dMRI) signal to realistic cell microstructure features. A crucial component of such simulations is th...
Transfer learning from 2D natural images to 4D fMRI brain images via geometric mapping [0.03%]
基于几何映射的迁移学习：从2D自然图象到4D脑fMRI图像
Kai Gao,Lubin Wang,Liang Li et al.
Kai Gao et al.
Functional magnetic resonance imaging (fMRI) allows real-time observation of brain activity through blood oxygen level-dependent (BOLD) signals and is extensively used in studies related to sex classification, age estimation, behavioral mea...
UNISELF: A unified network with instance normalization and self-ensembled lesion fusion for multiple sclerosis lesion segmentation [0.03%]
基于实例归一化和自集成病灶融合的多发性硬化症病灶分割统一网络
Jinwei Zhang,Lianrui Zuo,Blake E Dewey et al.
Jinwei Zhang et al.
Automated segmentation of multiple sclerosis (MS) lesions using multicontrast magnetic resonance (MR) images improves efficiency and reproducibility compared to manual delineation, with deep learning (DL) methods achieving state-of-the-art ...