Using a patch-wise m-net convolutional neural network for tissue segmentation in brain mri images
N Yamanakkanavar, B Lee - IEEE Access, 2020 - ieeexplore.ieee.org
Accurate segmentation of brain tissues, such as gray matter (GM), white matter (WM), and
cerebrospinal fluid (CSF), in magnetic resonance imaging (MRI) images, is helpful for the …
cerebrospinal fluid (CSF), in magnetic resonance imaging (MRI) images, is helpful for the …
Automated cervical spinal cord segmentation in real-world MRI of multiple sclerosis patients by optimized hybrid residual attention-aware convolutional neural …
A Bueno, I Bosch, A Rodríguez, A Jiménez… - Journal of Digital …, 2022 - Springer
Magnetic resonance (MR) imaging is the most sensitive clinical tool in the diagnosis and
monitoring of multiple sclerosis (MS) alterations. Spinal cord evaluation has gained interest …
monitoring of multiple sclerosis (MS) alterations. Spinal cord evaluation has gained interest …
MSMANet: A multi-scale mesh aggregation network for brain tumor segmentation
Y Zhang, Y Lu, W Chen, Y Chang, H Gu, B Yu - Applied Soft Computing, 2021 - Elsevier
The fine segmentation of brain tumor, which is instrumental in brain tumor diagnosis,
treatment planning and prognosis, is becoming a research hotspot in medical images …
treatment planning and prognosis, is becoming a research hotspot in medical images …
Feature fusion and latent feature learning guided brain tumor segmentation and missing modality recovery network
T Zhou - Pattern Recognition, 2023 - Elsevier
Accurate brain tumor segmentation is an essential step for clinical diagnosis and surgical
treatment. Multimodal brain tumor segmentation strongly relies on an effective fusion method …
treatment. Multimodal brain tumor segmentation strongly relies on an effective fusion method …
Nuclei segmentation of HE stained histopathological images based on feature global delivery connection network
P Shi, J Zhong, L Lin, L Lin, H Li, C Wu - Plos one, 2022 - journals.plos.org
The analysis of pathological images, such as cell counting and nuclear morphological
measurement, is an essential part in clinical histopathology researches. Due to the diversity …
measurement, is an essential part in clinical histopathology researches. Due to the diversity …
A hybrid deep learning framework for integrated segmentation and registration: evaluation on longitudinal white matter tract changes
To accurately analyze changes of anatomical structures in longitudinal imaging studies,
consistent segmentation across multiple time-points is required. Existing solutions often …
consistent segmentation across multiple time-points is required. Existing solutions often …
OctopusNet: a deep learning segmentation network for multi-modal medical images
Deep learning models, such as the fully convolutional network (FCN), have been widely
used in 3D biomedical segmentation and achieved state-of-the-art performance. Multiple …
used in 3D biomedical segmentation and achieved state-of-the-art performance. Multiple …
Volumetric white matter tract segmentation with nested self-supervised learning using sequential pretext tasks
Q Lu, Y Li, C Ye - Medical Image Analysis, 2021 - Elsevier
White matter (WM) tract segmentation based on diffusion magnetic resonance imaging
(dMRI) provides an important tool for the analysis of brain development, function, and …
(dMRI) provides an important tool for the analysis of brain development, function, and …
DH-GAC: Deep hierarchical context fusion network with modified geodesic active contour for multiple neurofibromatosis segmentation
Delineating accurately and simultaneously all lesions is vital and challenging for computer-
aided diagnosis for multiple neurofibromatosis (NF). However, existing CNN-based …
aided diagnosis for multiple neurofibromatosis (NF). However, existing CNN-based …
Automatic segmentation of vestibular schwannoma from T2-weighted MRI by deep spatial attention with hardness-weighted loss
Automatic segmentation of vestibular schwannoma (VS) tumors from magnetic resonance
imaging (MRI) would facilitate efficient and accurate volume measurement to guide patient …
imaging (MRI) would facilitate efficient and accurate volume measurement to guide patient …