The multimodal brain tumor image segmentation benchmark (BRATS)
In this paper we report the set-up and results of the Multimodal Brain Tumor Image
Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and …
Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and …
Automated brain tumor segmentation using multimodal brain scans: a survey based on models submitted to the BraTS 2012–2018 challenges
M Ghaffari, A Sowmya, R Oliver - IEEE reviews in biomedical …, 2019 - ieeexplore.ieee.org
Reliable brain tumor segmentation is essential for accurate diagnosis and treatment
planning. Since manual segmentation of brain tumors is a highly time-consuming, expensive …
planning. Since manual segmentation of brain tumors is a highly time-consuming, expensive …
Brain tumor detection using fusion of hand crafted and deep learning features
T Saba, AS Mohamed, M El-Affendi, J Amin… - Cognitive Systems …, 2020 - Elsevier
The perilous disease in the worldwide now a days is brain tumor. Tumor affects the brain by
damaging healthy tissues or intensifying intra cranial pressure. Hence, rapid growth in tumor …
damaging healthy tissues or intensifying intra cranial pressure. Hence, rapid growth in tumor …
Exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation
Deep learning networks have recently been shown to outperform other segmentation
methods on various public, medical-image challenge datasets, particularly on metrics …
methods on various public, medical-image challenge datasets, particularly on metrics …
Brain tumor segmentation using convolutional neural networks in MRI images
Among brain tumors, gliomas are the most common and aggressive, leading to a very short
life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the …
life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the …
Unsupervised detection of lesions in brain MRI using constrained adversarial auto-encoders
X Chen, E Konukoglu - arXiv preprint arXiv:1806.04972, 2018 - arxiv.org
Lesion detection in brain Magnetic Resonance Images (MRI) remains a challenging task.
State-of-the-art approaches are mostly based on supervised learning making use of large …
State-of-the-art approaches are mostly based on supervised learning making use of large …
A survey of MRI-based brain tumor segmentation methods
Brain tumor segmentation aims to separate the different tumor tissues such as active cells,
necrotic core, and edema from normal brain tissues of White Matter (WM), Gray Matter (GM) …
necrotic core, and edema from normal brain tissues of White Matter (WM), Gray Matter (GM) …
Molecular subtype classification of low‐grade gliomas using magnetic resonance imaging‐based radiomics and machine learning
In 2016, the World Health Organization (WHO) updated the glioma classification by
incorporating molecular biology parameters, including low‐grade glioma (LGG). In the new …
incorporating molecular biology parameters, including low‐grade glioma (LGG). In the new …
Brain tumor detection by using stacked autoencoders in deep learning
Brain tumor detection depicts a tough job because of its shape, size and appearance
variations. In this manuscript, a deep learning model is deployed to predict input slices as a …
variations. In this manuscript, a deep learning model is deployed to predict input slices as a …
Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels
Background Accurate segmentation of brain tumour in magnetic resonance images (MRI) is
a difficult task due to various tumour types. Using information and features from multimodal …
a difficult task due to various tumour types. Using information and features from multimodal …