The BraSyn 2023 challenge focuses on synthesizing missing MRI sequences for tumor segmentation. Different loss functions significantly affect synthesis quality, with Pix2Pix models trained under various penalties showing promising results. The study aims to optimize image synthesis performance by combining different learning objectives and loss functions.
Automatic localization and segmentation of brain tumors in MRI is crucial for diagnosis and monitoring. Deep learning-based algorithms require multiple input sequences, posing a challenge when a sequence is missing due to time constraints or artifacts. The BraSyn challenge addresses this issue by inviting researchers to synthesize missing MRI sequences using paired images-to-image translation frameworks like Pix2Pix.
The study investigates the effectiveness of Pix2Pix models trained under different loss functions for multi-sequence MR image synthesis. Results show that the choice of loss function significantly impacts synthesis quality, highlighting the need for careful selection in enhancing image realism. By combining various loss functions, optimal image synthesis performance can be achieved, benefiting downstream tasks like tumor segmentation.
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