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Found 5 papers, 2 papers with code
Multimodal CNN Networks for Brain Tumor Segmentation in MRI: A BraTS 2022 Challenge Solution
It is worth noting that our ensemble models took first place in the final evaluation on the BraTS testing dataset with Dice scores of 0. 9294, 0. 8788, and 0. 8803, and Hausdorf distance of 5. 23, 13. 54, and 12. 05, for the whole tumor, tumor core, and enhancing tumor, respectively.
Self-supervised iRegNet for the Registration of Longitudinal Brain MRI of Diffuse Glioma Patients
Reliable and accurate registration of patient-specific brain magnetic resonance imaging (MRI) scans containing pathologies is challenging due to tissue appearance changes.
The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients
Registration of longitudinal brain MRI scans containing pathologies is challenging due to dramatic changes in tissue appearance.
Ensemble CNN Networks for GBM Tumors Segmentation using Multi-parametric MRI
Accurate identification of the malignant brain tumor and its sub-regions is still one of the most challenging problems in medical image segmentation.
DeepSeg: Deep Neural Network Framework for Automatic Brain Tumor Segmentation using Magnetic Resonance FLAIR Images
Based on modified U-Net architecture, different CNN models such as Residual Neural Network (ResNet), Dense Convolutional Network (DenseNet), and NASNet have been utilized in this study.
