shalabh147/Brain-Tumor-Segmentation-and-Survival-Prediction-using-Deep-Neural-Networks
Use of state of the art Convolutional neural network architectures including 3D UNet, 3D VNet and 2D UNets for Brain Tumor Segmentation and using segmented image features for Survival Prediction of patients through deep neural networks.
This project helps medical professionals, specifically neuroradiologists and oncologists, analyze brain MRI scans to precisely identify and delineate different tumor components like necrosis, edema, and enhancing tumor. It takes multi-modal MRI images as input and produces detailed pixel-level segmentations of brain tumors. Additionally, it can contribute to predicting patient survival categories based on the image analysis and patient age.
171 stars. No commits in the last 6 months.
Use this if you need to automate or assist in the accurate segmentation of brain tumor sub-regions from MRI scans and want to explore features for patient survival prediction.
Not ideal if you are looking for a certified medical device for diagnosis, as this is a research implementation, or if you need to analyze image types other than brain MRIs.
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MIT
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Last pushed
Jul 24, 2020
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