Kelly Payette,Hongwei Bran Li,Priscille de Dumast et al.
Kelly Payette et al.
In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment ...
MUTE: A multilevel-stimulated denoising strategy for single cataractous retinal image dehazing [0.03%]
一种用于单张白内障视网膜图像去雾的多层刺激式降噪策略
Shuhe Zhang,Ashwin Mohan,Carroll A B Webers et al.
Shuhe Zhang et al.
In this research, we studied the duality between cataractous retinal image dehazing and image denoising and proposed that the dehazing task for cataractous retinal images can be achieved with the combination of image denoising and sigmoid f...
Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications [0.03%]
图中有图(GiG)：在非欧氏域中学习可解释的潜在图用于生物和医疗应用
Kamilia Zaripova,Luca Cosmo,Anees Kazi et al.
Kamilia Zaripova et al.
Graphs are a powerful tool for representing and analyzing unstructured, non-Euclidean data ubiquitous in the healthcare domain. Two prominent examples are molecule property prediction and brain connectome analysis. Importantly, recent works...
Automatic brain MRI motion artifact detection based on end-to-end deep learning is similarly effective as traditional machine learning trained on image quality metrics [0.03%]
基于端到端深度学习的自动脑部MRI运动伪影检测与传统基于图像质量度量的机器学习方法效果相似
Pál Vakli,Béla Weiss,János Szalma et al.
Pál Vakli et al.
Head motion artifacts in magnetic resonance imaging (MRI) are an important confounding factor concerning brain research as well as clinical practice. For this reason, several machine learning-based methods have been developed for the automa...
Deep multimodality-disentangled association analysis network for imaging genetics in neurodegenerative diseases [0.03%]
一种深层多模分解相关分析网络在神经退行性疾病影像遗传学中的应用
Tao Wang,Xiumei Chen,Jiawei Zhang et al.
Tao Wang et al.
Imaging genetics is a crucial tool that is applied to explore potentially disease-related biomarkers, particularly for neurodegenerative diseases (NDs). With the development of imaging technology, the association analysis between multimodal...
Anatomically constrained and attention-guided deep feature fusion for joint segmentation and deformable medical image registration [0.03%]
解剖约束和注意引导的深度特征融合在联合分割和医学图像配准中的应用
Hee Guan Khor,Guochen Ning,Yihua Sun et al.
Hee Guan Khor et al.
The main objective of anatomically plausible results for deformable image registration is to improve model's registration accuracy by minimizing the difference between a pair of fixed and moving images. Since many anatomical features are cl...
Gaussian Processes for real-time 3D motion and uncertainty estimation during MR-guided radiotherapy [0.03%]
基于实时3D运动和不确定性的MRI引导放射治疗中的高斯过程方法研究
Niek R F Huttinga,Tom Bruijnen,Cornelis A T van den Berg et al.
Niek R F Huttinga et al.
Respiratory motion during radiotherapy causes uncertainty in the tumor's location, which is typically addressed by an increased radiation area and a decreased dose. As a result, the treatments' efficacy is reduced. The recently proposed hyb...
ImUnity: A generalizable VAE-GAN solution for multicenter MR image harmonization [0.03%]
基于变分自编码器和生成对抗网络的多中心MR图像和谐化通用框架
Stenzel Cackowski,Emmanuel L Barbier,Michel Dojat et al.
Stenzel Cackowski et al.
ImUnity is an original 2.5D deep-learning model designed for efficient and flexible MR image harmonization. A VAE-GAN network, coupled with a confusion module and an optional biological preservation module, uses multiple 2D slices taken fro...
Cerebrovascular super-resolution 4D Flow MRI - Sequential combination of resolution enhancement by deep learning and physics-informed image processing to non-invasively quantify intracranial velocity, flow, and relative pressure [0.03%]
基于深度学习和物理信息图像处理的脑血管超分辨率4D流MRI：非侵入性量化颅内速度、流量和相对压力的序列组合
E Ferdian,D Marlevi,J Schollenberger et al.
E Ferdian et al.
The development of cerebrovascular disease is tightly coupled to regional changes in intracranial flow and relative pressure. Image-based assessment using phase contrast magnetic resonance imaging has particular promise for non-invasive ful...
BolT: Fused window transformers for fMRI time series analysis [0.03%]
基于融合窗口的变压器进行fMRI时间序列分析模型（BolT）
Hasan A Bedel,Irmak Sivgin,Onat Dalmaz et al.
Hasan A Bedel et al.
Deep-learning models have enabled performance leaps in analysis of high-dimensional functional MRI (fMRI) data. Yet, many previous methods are suboptimally sensitive for contextual representations across diverse time scales. Here, we presen...