Titlebook: Head and Neck Tumor Segmentation and Outcome Prediction; Second Challenge, HE Vincent Andrearczyk,Valentin Oreiller,Adrien Depeu Conference

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The Head and Neck Tumor Segmentation Based on 3D U-Net,head and neck tumor is the key step to make the appropriate radiotherapy schedule. However, it is a very time consuming and boring work. Therefore, automatic segmenting the head and neck tumor is of the very significant work. This paper adopts the U-Net network used in medical image segmentation com

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,Deep Learning Based GTV Delineation and?Progression Free Survival Risk Score Prediction for?Head anerapeutic dose and minimization of therapy induced damage to healthy tissue. Radiomics models have proven their power for detection of useful tumors characteristics that can be used for patient prognosis. We studied the ability of deep learning models for segmentation of gross tumor volumes (GTV) an

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PET/CT Head and Neck Tumor Segmentation and Progression Free Survival Prediction Using Deep and Macsing two 3D models for Head and Neck tumor segmentation in CT and FDG-PET images. A Progression Free Survival (PFS) prediction, based on a Gaussian Process Regression (GPR) model was design on Matlab. Radiomic features such as Haralick textures, geometrical and statistical data were extracted from t