VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images
Segmentation of key brain tissues from 3D medical images is of great significance for brain
disease diagnosis, progression assessment and monitoring of neurologic conditions. While …
disease diagnosis, progression assessment and monitoring of neurologic conditions. While …
Perspective of artificial intelligence in healthcare data management: A journey towards precision medicine
Mounting evidence has highlighted the implementation of big data handling and
management in the healthcare industry to improve the clinical services. Various private and …
management in the healthcare industry to improve the clinical services. Various private and …
Data augmentation using learned transformations for one-shot medical image segmentation
Image segmentation is an important task in many medical applications. Methods based on
convolutional neural networks attain state-of-the-art accuracy; however, they typically rely on …
convolutional neural networks attain state-of-the-art accuracy; however, they typically rely on …
Automatic multi-organ segmentation on abdominal CT with dense V-networks
Automatic segmentation of abdominal anatomy on computed tomography (CT) images can
support diagnosis, treatment planning, and treatment delivery workflows. Segmentation …
support diagnosis, treatment planning, and treatment delivery workflows. Segmentation …
Harmonization of cortical thickness measurements across scanners and sites
With the proliferation of multi-site neuroimaging studies, there is a greater need for handling
non-biological variance introduced by differences in MRI scanners and acquisition …
non-biological variance introduced by differences in MRI scanners and acquisition …
Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning
Remarkable progress has been made in image recognition, primarily due to the availability
of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs …
of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs …
Deep learning empowered volume delineation of whole-body organs-at-risk for accelerated radiotherapy
In radiotherapy for cancer patients, an indispensable process is to delineate organs-at-risk
(OARs) and tumors. However, it is the most time-consuming step as manual delineation is …
(OARs) and tumors. However, it is the most time-consuming step as manual delineation is …
Neuroanatomical changes observed over the course of a human pregnancy
Pregnancy is a period of profound hormonal and physiological changes experienced by
millions of women annually, yet the neural changes unfolding in the maternal brain …
millions of women annually, yet the neural changes unfolding in the maternal brain …
3D whole brain segmentation using spatially localized atlas network tiles
Detailed whole brain segmentation is an essential quantitative technique in medical image
analysis, which provides a non-invasive way of measuring brain regions from a clinical …
analysis, which provides a non-invasive way of measuring brain regions from a clinical …
3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study
This study investigates a 3D and fully convolutional neural network (CNN) for subcortical
brain structure segmentation in MRI. 3D CNN architectures have been generally avoided …
brain structure segmentation in MRI. 3D CNN architectures have been generally avoided …