Brain-Tumor-Segmentation/brain-tumor-segmentation-using-deep-neural-networks
The project presents a comparative study of Brain Tumor Segmentation using 3 approaches - 1) Sobel Operator and U-Net, 2) V-Net, 3) W-Net
This project helps radiologists and neurologists accurately identify and delineate brain tumors from MRI scans. You input multimodal MRI images (T1, T1ce, T2, FLAIR) from high-grade glioma patients, and it outputs segmented images highlighting the tumor regions: edema, enhancing tumor, and non-enhancing tumor. It's designed for medical professionals involved in brain tumor diagnosis and treatment planning.
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Use this if you need to precisely segment different regions of high-grade glioma brain tumors from patient MRI data to aid in diagnosis or treatment planning.
Not ideal if you are working with low-grade gliomas or other types of brain pathologies, as the models were specifically trained on high-grade glioma data.
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MIT
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Last pushed
Oct 11, 2021
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