Fig. 1
From: AI-based MRI auto-segmentation of brain tumor in rodents, a multicenter study
Flow chart and 3D U-Net architecture for current study. Collection and allocation of both data for model training, validation and test (A). All these data were manually segmented and pre-processed (B), followed by data augmentation and model training (C). The trained models were challenged by images with Gaussian noise added, measured quantitatively (D). AI-assisted segmentation was demonstrated based on ground truth by two radiologists (D). The same 3D U-Net architecture for training the two models was shown (E). Abbreviations: AI: artificial intelligence; RV: relative ratio; HD: Hausdorff distance; MSD: mean surface distance; DSC: Dice similarity coefficient