MMAN: Multi-modality aggregation network for brain segmentation from MR images

J Li, ZL Yu, Z Gu, H Liu, Y Li - Neurocomputing, 2019 - Elsevier
Brain tissue segmentation from Magnetic resonance (MR) image is significant for assessing
both neurologic conditions and brain disease. Manual brain tissue segmentation is time …

Automated thalamic nuclei segmentation using multi-planar cascaded convolutional neural networks

MS Majdi, MB Keerthivasan, BK Rutt, NM Zahr… - Magnetic resonance …, 2020 - Elsevier
Purpose To develop a fast and accurate convolutional neural network based method for
segmentation of thalamic nuclei. Methods A cascaded multi-planar scheme with a modified …

Neuron segmentation using deep complete bipartite networks

J Chen, S Banerjee, A Grama, WJ Scheirer… - … Conference on Medical …, 2017 - Springer
In this paper, we consider the problem of automatically segmenting neuronal cells in dual-
color confocal microscopy images. This problem is a key task in various quantitative analysis …

CSAF-CNN: Cross-layer spatial attention map fusion network for organ-at-risk segmentation in head and neck CT images

Z Liu, H Wang, W Lei, G Wang - 2020 IEEE 17th International …, 2020 - ieeexplore.ieee.org
Accurate segmentation of organ at risk (OARs) in head and neck CT images is critical for
planning of radiotherapy of the nasopharynx cancer. In segmentation tasks, fully …

A 3D spatially weighted network for segmentation of brain tissue from MRI

L Sun, W Ma, X Ding, Y Huang… - IEEE transactions on …, 2019 - ieeexplore.ieee.org
The segmentation of brain tissue in MRI is valuable for extracting brain structure to aid
diagnosis, treatment and tracking the progression of different neurologic diseases. Medical …

A deep dense residual network with reduced parameters for volumetric brain tissue segmentation from MR images

R Basnet, MO Ahmad, MNS Swamy - Biomedical Signal Processing and …, 2021 - Elsevier
Deep convolutional neural networks (DCNN) have proven to be the state-of-the-art methods
for brain tissue segmentation; however, their complex architectures, and the large number of …

A joint 3d+ 2d fully convolutional framework for subcortical segmentation

J Wu, Y Zhang, X Tang - … Conference, Shenzhen, China, October 13–17 …, 2019 - Springer
In this paper, we proposed and validated a novel joint 3D+ 2D fully convolutional framework
for segmenting subcortical structures from magnetic resonance images (MRIs). A 2D …

Axial attention convolutional neural network for brain tumor segmentation with multi-modality MRI scans

W Tian, D Li, M Lv, P Huang - Brain sciences, 2022 - mdpi.com
Accurately identifying tumors from MRI scans is of the utmost importance for clinical
diagnostics and when making plans regarding brain tumor treatment. However, manual …

Reproducible white matter tract segmentation using 3D U-Net on a large-scale DTI dataset

B Li, M de Groot, MW Vernooij, MA Ikram… - Machine Learning in …, 2018 - Springer
Tract-specific diffusion measures, as derived from brain diffusion MRI, have been linked to
white matter tract structural integrity and neurodegeneration. As a consequence, there is a …

Random fourier features-based deep learning improvement with class activation interpretability for nerve structure segmentation

CA Jimenez-Castaño, AM Álvarez-Meza… - Sensors, 2021 - mdpi.com
Peripheral nerve blocking (PNB) is a standard procedure to support regional anesthesia.
Still, correct localization of the nerve's structure is needed to avoid adverse effects; thereby …