Automatic brain tumor segmentation based on cascaded convolutional neural networks with uncertainty estimation
Automatic segmentation of brain tumors from medical images is important for clinical
assessment and treatment planning of brain tumors. Recent years have seen an increasing …
assessment and treatment planning of brain tumors. Recent years have seen an increasing …
HDC-Net: Hierarchical decoupled convolution network for brain tumor segmentation
Accurate segmentation of brain tumor from magnetic resonance images (MRIs) is crucial for
clinical treatment decision and surgical planning. Due to the large diversity of the tumors and …
clinical treatment decision and surgical planning. Due to the large diversity of the tumors and …
Automatic brain tumor segmentation using convolutional neural networks with test-time augmentation
Automatic brain tumor segmentation plays an important role for diagnosis, surgical planning
and treatment assessment of brain tumors. Deep convolutional neural networks (CNNs) …
and treatment assessment of brain tumors. Deep convolutional neural networks (CNNs) …
One-pass multi-task convolutional neural networks for efficient brain tumor segmentation
The model cascade strategy that runs a series of deep models sequentially for coarse-to-fine
medical image segmentation is becoming increasingly popular, as it effectively relieves the …
medical image segmentation is becoming increasingly popular, as it effectively relieves the …
Learning contextual and attentive information for brain tumor segmentation
Thanks to the powerful representation learning ability, convolutional neural network has
been an effective tool for the brain tumor segmentation task. In this work, we design multiple …
been an effective tool for the brain tumor segmentation task. In this work, we design multiple …
Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution
Brain tumor segmentation is a critical task for patient's disease management. In order to
automate and standardize this task, we trained multiple U-net like neural networks, mainly …
automate and standardize this task, we trained multiple U-net like neural networks, mainly …
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 …
Improving patch-based convolutional neural networks for MRI brain tumor segmentation by leveraging location information
The manual brain tumor annotation process is time consuming and resource consuming,
therefore, an automated and accurate brain tumor segmentation tool is greatly in demand. In …
therefore, an automated and accurate brain tumor segmentation tool is greatly in demand. In …
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 …
Region-based evidential deep learning to quantify uncertainty and improve robustness of brain tumor segmentation
Despite recent advances in the accuracy of brain tumor segmentation, the results still suffer
from low reliability and robustness. Uncertainty estimation is an efficient solution to this …
from low reliability and robustness. Uncertainty estimation is an efficient solution to this …