BraSyn 2023 challenge: Missing MRI synthesis and the effect of different learning objectives
arxiv(2024)
摘要
This work is addressing the Brain Magnetic Resonance Image Synthesis for
Tumor Segmentation (BraSyn) challenge which was hosted as part of the Brain
Tumor Segmentation challenge (BraTS) 2023. In this challenge researchers are
invited to work on synthesizing a missing magnetic resonance image sequence
given other available sequences to facilitate tumor segmentation pipelines
trained on complete sets of image sequences. This problem can be addressed
using deep learning in the framework of paired images-to-image translation. In
this work, we proposed to investigate the effectiveness of a commonly-used deep
learning framework such as Pix2Pix trained under supervision of different
image-quality loss functions. Our results indicate that using different loss
functions significantly affects the synthesis quality. We systematically study
the impact of different loss functions in the multi-sequence MR image synthesis
setting of the BraSyn challenge. Furthermore, we show how image synthesis
performance can be optimized by beneficially combining different learning
objectives.
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