Titlebook: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries; 5th International Wo Alessandro Crimi,Spyridon Bakas Conferen

Medicare

Multidimensional and Multiresolution Ensemble Networks for Brain Tumor Segmentationnce with DICE scores of 0.898, 0.784, 0.779 for the whole tumor (WT), tumor core (TC), and enhancing tumor (ET), respectively and an accuracy of 34.5% for predicting survival. The Ensemble of multiresolution 2D networks achieved 88.75%, 83.28% and 79.34% dice for WT, TC, and ET respectively in a test dataset of 166 subjects.

LAST

Two Stages CNN-Based Segmentation of Gliomas, Uncertainty Quantification and Prediction of Overall Prks. The prediction stage is implemented using kernel principal component analysis and random forest classifiers. It only requires a predicted segmentation of the tumor and a homemade atlas. Its simplicity allows to train it with very few examples and it can be used after any segmentation process.