Brain tumor segmentation using deep fully convolutional neural networks
In this study, brain tumor substructures are segmented using 2D fully convolutional neural networks. A number of modifications such as double convolution layers, inception modules, and dense modules were added to a U-Net to achieve a deep architecture and test if the increased depth improves the performance. The experiments show that the deep architectures improve the performance. Also, the performance is enhanced from ensembling across the models trained on images in different orientations and ensembling across the models with different architectures. Even without any data augmentation, the ensembled model achieves a competitive performance and generalizes well on a new dataset. The resulting mean 3D Dice scores (ET/WT/TC) on the BRATS17 validation and test sets are 0.75/0.88/0.73 and 0.72/0.86/0.73.
Kim, Geena, "Brain tumor segmentation using deep fully convolutional neural networks" (2018). Regis University Faculty Publications. 366.