Semi-supervised medical image segmentation based on dual swap data mixing and cross EMA strategies [0.03%]
基于双交换数据混合和交叉EMA策略的半监督医学图像分割
Licheng Zheng,Lihui Wang,Yingfeng Ou et al.
Licheng Zheng et al.
Background: Semi-supervised medical image segmentation methods based on mean teacher (MT) framework provide a promising means for addressing the dense prediction problems with limited annotated images and numerous unlabel...
Comparative analysis of residual setup errors in head and neck patients from upright versus supine radiotherapy postures [0.03%]
坐位与仰卧位放疗头颈部患者残余摆位误差的比较分析
Jiayao Sun,Lijia Zhang,Weiwei Wang et al.
Jiayao Sun et al.
Background: Carbon-ion rotating gantries use is limited by its large size, weight, and high cost. Gantry-free modality enables the reduction of the overall size, weight, and cost. Among them, upright treatment, which util...
Advancing cardiac MRI multi-structure segmentation: A semi-supervised multidimensional consistency constraint learning network [0.03%]
基于半监督多维度一致性的约束学习网络在心脏磁共振图像分割中的应用研究
Hongzhen Cui,Meihua Piao,Xinghe Huang et al.
Hongzhen Cui et al.
Background: Deep convolutional neural networks (DCNNs) have been proposed for medical Magnetic Resonance Imaging (MRI) segmentation, but their effectiveness is often limited by challenges in semantic discrimination, bound...
Improving decomposition image quality in dual-energy chest radiography using two-dimensional crisscrossed anti-scatter grid [0.03%]
使用二维交叉散射栅格改善双能量胸部摄影分解图像质量
Duhee Jeon,Younghwan Lim,Hyesun Yang et al.
Duhee Jeon et al.
Background: Chest radiography is a widely used medical imaging modality for diagnosing chest-related diseases. However, anatomical structure overlap hinders accurate lesion detection. While the dual-energy x-ray imaging t...
Semantic-consistent diffusion model for unsupervised traumatic brain injury detection and segmentation from computed tomography images [0.03%]
一种用于无监督检测和分割CT图像中的脑外伤的语义一致性扩散模型
Diya Sun,Yuru Pei,Liyi Ying et al.
Diya Sun et al.
Background: Unsupervised traumatic brain injury (TBI) lesion detection aims to identify and segment abnormal regions, such as cerebral edema and hemorrhages, using only healthy training data. Recent advancements in genera...
Tissue classification from raw diffusion-weighted images using machine learning [0.03%]
基于机器学习的原始扩散加权图像组织分类方法
Guangyu Dan,Cui Feng,Zheng Zhong et al.
Guangyu Dan et al.
Background: In diffusion-weighted imaging (DWI), a large collection of diffusion models is available to provide insights into tissue characteristics. However, these models are limited by predefined assumptions and computa...
Deep learning-based estimation of respiration-induced deformation from surface motion: A proof-of-concept study on 4D thoracic image synthesis [0.03%]
基于深度学习的呼吸运动所致形变的表面运动估算：胸部四维图像合成的概念验证性研究
Jie Zhang,Xue Bai,Guoping Shan
Jie Zhang
Background: Four-dimension computed tomography (4D-CT) provides important respiration-related information for thoracic radiotherapy. Its quality is challenged by various respiratory patterns. Its acquisition gives rise to...
Joao Seco,Joseph O Deasy,Indra J Das
Joao Seco
Hua Bai,Jieyu Liu,Chen Wu et al.
Hua Bai et al.
Background: Meningiomas are the most common primary intracranial tumors in adults. Low-grade meningiomas have a low recurrence rate, whereas high-grade meningiomas are highly aggressive and recurrent. Therefore, the patho...
Out-of-field neutron radiation from clinical proton, helium, carbon, and oxygen ion beams [0.03%]
临床质子、氦、碳和氧离子束的场外中子辐射
Matteo Bolzonella,Marco Caresana,Andrea Cirillo et al.
Matteo Bolzonella et al.
Background: In hadron therapy, out-of-field doses, which may in the long-term cause secondary cancers, are mostly due to neutrons. Very recently, 4He and 16O beams have been added to protons and 12C ions for clinical ther...