Current and emerging trends in medical image segmentation with deep learning
PH Conze, G Andrade-Miranda… - … on Radiation and …, 2023 - ieeexplore.ieee.org
In recent years, the segmentation of anatomical or pathological structures using deep
learning has experienced a widespread interest in medical image analysis. Remarkably …
learning has experienced a widespread interest in medical image analysis. Remarkably …
Medical image segmentation on mri images with missing modalities: A review
R Azad, N Khosravi, M Dehghanmanshadi… - arXiv preprint arXiv …, 2022 - arxiv.org
Dealing with missing modalities in Magnetic Resonance Imaging (MRI) and overcoming
their negative repercussions is considered a hurdle in biomedical imaging. The combination …
their negative repercussions is considered a hurdle in biomedical imaging. The combination …
[HTML][HTML] Deep learning based brain tumor segmentation: a survey
Brain tumor segmentation is one of the most challenging problems in medical image
analysis. The goal of brain tumor segmentation is to generate accurate delineation of brain …
analysis. The goal of brain tumor segmentation is to generate accurate delineation of brain …
D2-Net: Dual Disentanglement Network for Brain Tumor Segmentation With Missing Modalities
Multi-modal Magnetic Resonance Imaging (MRI) can provide complementary information for
automatic brain tumor segmentation, which is crucial for diagnosis and prognosis. While …
automatic brain tumor segmentation, which is crucial for diagnosis and prognosis. While …
SMU-Net: Style matching U-Net for brain tumor segmentation with missing modalities
Gliomas are one of the most prevalent types of primary brain tumors, accounting for more
than 30% of all cases and they develop from the glial stem or progenitor cells. In theory, the …
than 30% of all cases and they develop from the glial stem or progenitor cells. In theory, the …
Dpafnet: A residual dual-path attention-fusion convolutional neural network for multimodal brain tumor segmentation
Y Chang, Z Zheng, Y Sun, M Zhao, Y Lu… - … Signal Processing and …, 2023 - Elsevier
Brain tumors are highly hazardous, and precise automated segmentation of brain tumor
subregions has great importance and research significance on the diagnosis and treatment …
subregions has great importance and research significance on the diagnosis and treatment …
M3AE: Multimodal representation learning for brain tumor segmentation with missing modalities
Multimodal magnetic resonance imaging (MRI) provides complementary information for sub-
region analysis of brain tumors. Plenty of methods have been proposed for automatic brain …
region analysis of brain tumors. Plenty of methods have been proposed for automatic brain …
Two-branch network for brain tumor segmentation using attention mechanism and super-resolution reconstruction
Z Jia, H Zhu, J Zhu, P Ma - Computers in Biology and Medicine, 2023 - Elsevier
Accurate segmentation of brain tumor plays an important role in MRI diagnosis and
treatment monitoring of brain tumor. However, the degree of lesions in each patient's brain …
treatment monitoring of brain tumor. However, the degree of lesions in each patient's brain …
Feature-enhanced generation and multi-modality fusion based deep neural network for brain tumor segmentation with missing MR modalities
Abstract Using multimodal Magnetic Resonance Imaging (MRI) is necessary for accurate
brain tumor segmentation. The main problem is that not all types of MRIs are always …
brain tumor segmentation. The main problem is that not all types of MRIs are always …
A literature survey of MR-based brain tumor segmentation with missing modalities
Multimodal MR brain tumor segmentation is one of the hottest issues in the community of
medical image processing. However, acquiring the complete set of MR modalities is not …
medical image processing. However, acquiring the complete set of MR modalities is not …