Advancing Brain Tumor Segmentation with Optimal Data Augmentation

Segmentation of the brain tumor on MRI is key to the exact diagnosis and treatment planning of patients suffering from a brain tumor. The process is also quite time-consuming and varies depending on the radiologist. For this reason, robust and accurate models for automated segmentation of brain tumors are in high demand. Through the following project work, robust and accurate brain tumor segmentation models are developed by the implementation of ResUNET and U-Net architectures and augmentation the data through optimized techniques. The hybrid model uses residual connections of ResUNET for deep feature extraction and the encoder-decoder structure of U-Net for precise segmentation. Elastic deformation techniques, in addition to augmentation parameters such as rotation, zooming, and changes in brightness, were simulated to mimic realistic variations in the tumor. Experiments show that the model trained with augmentation significantly outperforms the model trained without augmentation in terms of Dice score and IoU, showing better accuracy and robustness in segmentation. The model was trained and tested on the BraTS 2020 MRI dataset, in terms of performance evaluated by the Dice coefficient and Intersection over Union (IoU). In this work, the benefit of the association with advanced architectures to targeted augmentation techniques is demonstrated towards brain tumor segmentation, and hopes for it to support the work of radiologists in clinical settings in accurate identification and delimitation of boundaries in tumor MRI scans.