Abstract
Three 2D CNN (Convolutional Neuronal Networks) models, one for each patient-plane (axial, sagittal and coronal) plus two 3D CNN models were ensemble using 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 the automatic segmentation. A 35-feature selection process was performed over 1000 different features. An anti-concordant prediction was made based on the Kaplan-Meier estimator.
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Martinez-Larraz, A., Martí Asenjo, J., Álvarez Rodríguez, B. (2022). PET/CT Head and Neck Tumor Segmentation and Progression Free Survival Prediction Using Deep and Machine Learning Techniques. In: Andrearczyk, V., Oreiller, V., Hatt, M., Depeursinge, A. (eds) Head and Neck Tumor Segmentation and Outcome Prediction. HECKTOR 2021. Lecture Notes in Computer Science, vol 13209. Springer, Cham. https://doi.org/10.1007/978-3-030-98253-9_16
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