An Optimization Framework for Processing and Transfer Learning for the
  Brain Tumor Segmentation
Tianyi Ren Ethan Honey Harshitha Rebala Abhishek Sharma Agamdeep Chopra Mehmet Kurt
Tumor segmentation from multi-modal brain MRI images is a challenging task
due to the limited samples, high variance in shapes and uneven distribution of
tumor morphology. The performance of automated medical image segmentation has
been significant improvement by the recent advances in deep learning. However,
the model predictions have not yet reached the desired level for clinical use
in terms of accuracy and generalizability. In order to address the distinct
problems presented in Challenges 1, 2, and 3 of BraTS 2023, we have constructed
an optimization framework based on a 3D U-Net model for brain tumor
segmentation. This framework incorporates a range of techniques, including
various pre-processing and post-processing techniques, and transfer learning.
On the validation datasets, this multi-modality brain tumor segmentation
framework achieves an average lesion-wise Dice score of 0.79, 0.72, 0.74 on
Challenges 1, 2, 3 respectively.