BraSyn 2023 challenge: Missing MRI synthesis and the effect of different learning objectives
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866914717250879488 |
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| author | Baltruschat, Ivo M. Janbakhshi, Parvaneh Lenga, Matthias |
| author_facet | Baltruschat, Ivo M. Janbakhshi, Parvaneh Lenga, Matthias |
| contents | This work addresses the Brain Magnetic Resonance Image Synthesis for Tumor Segmentation (BraSyn) challenge, which was hosted as part of the Brain Tumor Segmentation (BraTS) challenge in 2023. In this challenge, researchers are invited to synthesize 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 tackled using deep learning within the framework of paired image-to-image translation. In this study, we propose investigating the effectiveness of a commonly used deep learning framework, such as Pix2Pix, trained under the supervision of different image-quality loss functions. Our results indicate that the use of different loss functions significantly affects the synthesis quality. We systematically study the impact of various loss functions in the multi-sequence MR image synthesis setting of the BraSyn challenge. Furthermore, we demonstrate how image synthesis performance can be optimized by combining different learning objectives beneficially. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_07800 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BraSyn 2023 challenge: Missing MRI synthesis and the effect of different learning objectives Baltruschat, Ivo M. Janbakhshi, Parvaneh Lenga, Matthias Image and Video Processing Computer Vision and Pattern Recognition This work addresses the Brain Magnetic Resonance Image Synthesis for Tumor Segmentation (BraSyn) challenge, which was hosted as part of the Brain Tumor Segmentation (BraTS) challenge in 2023. In this challenge, researchers are invited to synthesize 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 tackled using deep learning within the framework of paired image-to-image translation. In this study, we propose investigating the effectiveness of a commonly used deep learning framework, such as Pix2Pix, trained under the supervision of different image-quality loss functions. Our results indicate that the use of different loss functions significantly affects the synthesis quality. We systematically study the impact of various loss functions in the multi-sequence MR image synthesis setting of the BraSyn challenge. Furthermore, we demonstrate how image synthesis performance can be optimized by combining different learning objectives beneficially. |
| title | BraSyn 2023 challenge: Missing MRI synthesis and the effect of different learning objectives |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.07800 |