3D nnU-Net with Multi Loss Ensembles for Automated Segmentation of Intracranial Aneurysm

In the segmentation of intracranial aneurysm, deep neural networks are equipped with modified loss functions to penalize the training weights for aneurysm false predictions and conduct unbiased learning. In this paper, we used a new compound loss function to capture the different aspects of embedding as well as diverse features. The proposed loss was given to a 3D full resolution nnU-Net to segment imbalanced TOF-MRA images from ADAM dataset. The proposed loss outperformed commonly used losses in terms of Dice, Sensitivity, and Precision.