A systematic review of automated methods to perform white matter tract segmentation
White matter tract segmentation is a pivotal research area that leverages diffusion-weighted
magnetic resonance imaging (dMRI) for the identification and mapping of individual white …
magnetic resonance imaging (dMRI) for the identification and mapping of individual white …
Adaptive Feature Medical Segmentation Network: an adaptable deep learning paradigm for high-performance 3D brain lesion segmentation in medical imaging
Introduction In neurological diagnostics, accurate detection and segmentation of brain
lesions is crucial. Identifying these lesions is challenging due to its complex morphology …
lesions is crucial. Identifying these lesions is challenging due to its complex morphology …
[HTML][HTML] Classifyber, a robust streamline-based linear classifier for white matter bundle segmentation
Virtual delineation of white matter bundles in the human brain is of paramount importance
for multiple applications, such as pre-surgical planning and connectomics. A substantial …
for multiple applications, such as pre-surgical planning and connectomics. A substantial …
Global channel attention networks for intracranial vessel segmentation
Intracranial blood vessel segmentation plays an essential role in the diagnosis and surgical
planning of cerebrovascular diseases. Recently, deep convolutional neural networks have …
planning of cerebrovascular diseases. Recently, deep convolutional neural networks have …
RFNet: Region-aware fusion network for incomplete multi-modal brain tumor segmentation
Most existing brain tumor segmentation methods usually exploit multi-modal magnetic
resonance imaging (MRI) images to achieve high segmentation performance. However, the …
resonance imaging (MRI) images to achieve high segmentation performance. However, the …
MM-BiFPN: multi-modality fusion network with Bi-FPN for MRI brain tumor segmentation
For medical imaging tasks, it is a prevalent practice to have a multi-modality image dataset,
as experts prefer using multiple medical devices to diagnose a disease. Each device can …
as experts prefer using multiple medical devices to diagnose a disease. Each device can …
Claw u-net: A unet variant network with deep feature concatenation for scleral blood vessel segmentation
Sturge-Weber syndrome (SWS) is a vascular malformation disease, and it may cause
blindness if the patient's condition is severe. Clinical results show that SWS can be divided …
blindness if the patient's condition is severe. Clinical results show that SWS can be divided …
RP-Net: a 3D convolutional neural network for brain segmentation from magnetic resonance imaging
L Wang, C Xie, N Zeng - IEEE Access, 2019 - ieeexplore.ieee.org
Quantitative analysis of brain volume is quite significant for the diagnosis of brain diseases.
Accurate segmentation of essential brain tissues from 3D medical images is fundamental to …
Accurate segmentation of essential brain tissues from 3D medical images is fundamental to …
HMNet: Hierarchical multi-scale brain tumor segmentation network
An accurate and efficient automatic brain tumor segmentation algorithm is important for
clinical practice. In recent years, there has been much interest in automatic segmentation …
clinical practice. In recent years, there has been much interest in automatic segmentation …
Automatic segmentation of brain MRI using a novel patch-wise U-net deep architecture
B Lee, N Yamanakkanavar, JY Choi - Plos one, 2020 - journals.plos.org
Accurate segmentation of brain magnetic resonance imaging (MRI) is an essential step in
quantifying the changes in brain structure. Deep learning in recent years has been …
quantifying the changes in brain structure. Deep learning in recent years has been …