Brain tumor segmentation of MRI images: A comprehensive review on the application of artificial intelligence tools
Background Brain cancer is a destructive and life-threatening disease that imposes
immense negative effects on patients' lives. Therefore, the detection of brain tumors at an …
immense negative effects on patients' lives. Therefore, the detection of brain tumors at an …
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 …
Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge
Gliomas are the most common primary brain malignancies, with different degrees of
aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, ie …
aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, ie …
Multimodal brain tumor detection and classification using deep saliency map and improved dragonfly optimization algorithm
In the last decade, there has been a significant increase in medical cases involving brain
tumors. Brain tumor is the tenth most common type of tumor, affecting millions of people …
tumors. Brain tumor is the tenth most common type of tumor, affecting millions of people …
A deep multi-task learning framework for brain tumor segmentation
H Huang, G Yang, W Zhang, X Xu, W Yang… - Frontiers in …, 2021 - frontiersin.org
Glioma is the most common primary central nervous system tumor, accounting for about half
of all intracranial primary tumors. As a non-invasive examination method, MRI has an …
of all intracranial primary tumors. As a non-invasive examination method, MRI has an …
TranSiam: Aggregating multi-modal visual features with locality for medical image segmentation
Automatic segmentation of medical images plays an important role in the diagnosis of
diseases. On single-modal data, convolutional neural networks have demonstrated …
diseases. On single-modal data, convolutional neural networks have demonstrated …
Deep learning techniques for tumor segmentation: a review
Recently, deep learning, especially convolutional neural networks, has achieved the
remarkable results in natural image classification and segmentation. At the same time, in the …
remarkable results in natural image classification and segmentation. At the same time, in the …
Canet: Context aware network for brain glioma segmentation
Automated segmentation of brain glioma plays an active role in diagnosis decision,
progression monitoring and surgery planning. Based on deep neural networks, previous …
progression monitoring and surgery planning. Based on deep neural networks, previous …
RMU-net: a novel residual mobile U-net model for brain tumor segmentation from MR images
The most aggressive form of brain tumor is gliomas, which leads to concise life when high
grade. The early detection of glioma is important to save the life of patients. MRI is a …
grade. The early detection of glioma is important to save the life of patients. MRI is a …
CMM-Net: Contextual multi-scale multi-level network for efficient biomedical image segmentation
MA Al-Masni, DH Kim - Scientific reports, 2021 - nature.com
Medical image segmentation of tissue abnormalities, key organs, or blood vascular system
is of great significance for any computerized diagnostic system. However, automatic …
is of great significance for any computerized diagnostic system. However, automatic …