Brain Segmentation
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Updated
Oct 24, 2021 - Python
Brain Segmentation
Implementation of the Mean Teacher method for brain lesion segmentation based on DeepMedic, from paper published in IPMI 2019
Useful functions and pipelines for brain tumor segmentation.
Brain Tumor Segmentation Pipeline for BraTS Challenge
We segmented the Brain tumor using Brats dataset and as we know it is in 3D format we used the slicing method in which we slice the images in 2D form according to its 3 axis and then giving the model for training then combining waits to segment brain tumor. We used UNET model for training our dataset.
Optimized U-Net for Brain Tumor Segmentation
A modular, 3D unet built in keras for 3D medical image segmentation. Also includes useful classes for extracting and training on 3D patches for data augmentation or memory efficiency.
Multimodal Brain Tumor Segmentation Boosted by Monomodal Normal Brain Images
Repository with models, experiments and approaches for the BraTS 2017 and iSeg segmentation challenges.
Interactive Brain Tumor Segmentation with FocalClick and CDNet
Code for automated brain tumor segmentation from MRI scans using CNNs with attention mechanisms, deep supervision, and Swin-Transformers. Based on my Master's dissertation project at Brunel University, it features 3 deep learning models, showcasing integration of advanced techniques in medical image analysis.
Creating a U-Net In PyTorch to segment the BraTS 2020 dataset
Official BraTS 2023 Segmentation Performance Metrics
[MIDL 2023] MMCFormer: Missing Modality Compensation Transformer for Brain Tumor Segmentation
Solution of the RSNA/ASNR/MICCAI Brain Tumor Segmentation (BraTS) Challenge 2021
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