Synthesizing Missing MRI Sequences Towards Reliable Brain Tumor Segmentation Using Deep Learning

Motivation: Automated and robust segmentation of brain tumors requires multiple MRI sequences and can expedite neuro-oncological clinical trials. Goal(s): Our goal was to develop a deep learning model for brain tumor segmentation, even when some MRI sequences are missing. Approach: We enhanced a GAN with attention modules to synthesize missing sequences and employed an optimized nnU-Net for segmentation using both real and synthesized sequences. Results: The proposed AI-based model significantly improved brain tumor segmentation, with overall Dice scores increasing from 0.688% when FLAIR is missing to 0.873% using synthesized FLAIR derived from T2, and achieving 0.901% with real FLAIR. Impact: The developed two-stage deep learning framework, comprising synthesis and segmentation, enhances segmentation of brain tumors in MRI, especially when real sequences are unavailable. This advancement accelerates clinical trials and reduces manual segmentation time, yielding promising results.