ABSTRACT Background and Objective Manual brain tumor segmentation by radiologists is
time consuming and subjective. Therefore, fully automatic segmentation of different brain …

Segmentation of brain tissue types from diffusion MRI (dMRI) is an important task, required
for quantification of brain microstructure and for improving tractography. Current dMRI …

Brain image segmentation is used for visualizing and quantifying anatomical structures of
the brain. We present an automated approach using 2D deep residual dilated networks …

Convolutional neural networks (CNNs) for brain tumor segmentation are generally
developed using complete sets of magnetic resonance imaging (MRI) sequences for both …

Background Accurate and automated brain tumor segmentation from multi‐modality MR
images plays a significant role in tumor treatment. However, the existing approaches mainly …

The popularity of fluorescent labelling and mesoscopic optical imaging techniques enable
the acquisition of whole mammalian brain vasculature images at capillary resolution …

Introduction The application of U-shaped convolutional neural network (CNN) methods in
medical image segmentation tasks has yielded impressive results. However, this structure's …

We present a novel deep learning model equipped with a new region-aware global context
modeling technique for automatic nerve segmentation from ultrasound images, which is a …

White matter tractography mapping is a must in neuro-surgical planning and navigation to
minimize risks of iatrogenic damages. Clinical tractography pipelines still require time …

In neuro-imaging, MRI is commonly used to acquire multiple sequences simultaneously,
including T1, T2 and FLAIR. Multimodal image segmentation involves learning an optimal …