Overview of multi-modal brain tumor mr image segmentation
The precise segmentation of brain tumor images is a vital step towards accurate diagnosis
and effective treatment of brain tumors. Magnetic Resonance Imaging (MRI) can generate …
and effective treatment of brain tumors. Magnetic Resonance Imaging (MRI) can generate …
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 …
Data augmentation for brain-tumor segmentation: a review
Data augmentation is a popular technique which helps improve generalization capabilities
of deep neural networks, and can be perceived as implicit regularization. It plays a pivotal …
of deep neural networks, and can be perceived as implicit regularization. It plays a pivotal …
Automatic brain tumor segmentation with scale attention network
Y Yuan - Brainlesion: Glioma, Multiple Sclerosis, Stroke and …, 2021 - Springer
Automatic segmentation of brain tumors is an essential but challenging step for extracting
quantitative imaging biomarkers for accurate tumor detection, diagnosis, prognosis …
quantitative imaging biomarkers for accurate tumor detection, diagnosis, prognosis …
NestedFormer: Nested modality-aware transformer for brain tumor segmentation
Multi-modal MR imaging is routinely used in clinical practice to diagnose and investigate
brain tumors by providing rich complementary information. Previous multi-modal MRI …
brain tumors by providing rich complementary information. Previous multi-modal MRI …
Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks
A cascade of fully convolutional neural networks is proposed to segment multi-modal
Magnetic Resonance (MR) images with brain tumor into background and three hierarchical …
Magnetic Resonance (MR) images with brain tumor into background and three hierarchical …
A multi-path adaptive fusion network for multimodal brain tumor segmentation
Y Ding, L Gong, M Zhang, C Li, Z Qin - Neurocomputing, 2020 - Elsevier
The deep learning method has shown its outstanding performance in object recognition and
becomes the first choice for medical image analysis. However, how to effectively propagate …
becomes the first choice for medical image analysis. However, how to effectively propagate …
A novel end-to-end brain tumor segmentation method using improved fully convolutional networks
Accurate brain magnetic resonance imaging (MRI) tumor segmentation continues to be an
active research topic in medical image analysis since it provides doctors with meaningful …
active research topic in medical image analysis since it provides doctors with meaningful …
One-pass multi-task networks with cross-task guided attention for brain tumor segmentation
Class imbalance has emerged as one of the major challenges for medical image
segmentation. The model cascade (MC) strategy, a popular scheme, significantly alleviates …
segmentation. The model cascade (MC) strategy, a popular scheme, significantly alleviates …
MBANet: A 3D convolutional neural network with multi-branch attention for brain tumor segmentation from MRI images
Y Cao, W Zhou, M Zang, D An, Y Feng, B Yu - … Signal Processing and …, 2023 - Elsevier
More than half of brain tumors are malignant tumors, so there is a need for fast and accurate
segmentation of tumor regions in brain Magnetic Resonance Imaging (MRI) images …
segmentation of tumor regions in brain Magnetic Resonance Imaging (MRI) images …