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Yu, Z.* ; Zhao, B.* ; Zhang, S.* ; Chen, X.* ; Yan, F.* ; Feng, J.* ; Peng, T. ; Zhang, X.Y.*

HiFi-Syn: Hierarchical granularity discrimination for high-fidelity synthesis of MR images with structure preservation.

Med. Image Anal. 100:103390 (2024)

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Open Access Green: Postprint online available 12/2026

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Synthesizing medical images while preserving their structural information is crucial in medical research. In such scenarios, the preservation of anatomical content becomes especially important. Although recent advances have been made by incorporating instance-level information to guide translation, these methods overlook the spatial coherence of structural-level representation and the anatomical invariance of content during translation. To address these issues, we introduce hierarchical granularity discrimination, which exploits various levels of semantic information present in medical images. Our strategy utilizes three levels of discrimination granularity: pixel-level discrimination using a Brain Memory Bank, structure-level discrimination on each brain structure with a re-weighting strategy to focus on hard samples, and global-level discrimination to ensure anatomical consistency during translation. The image translation performance of our strategy has been evaluated on three independent datasets (UK Biobank, IXI, and BraTS 2018), and it has outperformed state-of-the-art algorithms. Particularly, our model excels not only in synthesizing normal structures but also in handling abnormal (pathological) structures, such as brain tumors, despite the variations in contrast observed across different imaging modalities due to their pathological characteristics. The diagnostic value of synthesized MR images containing brain tumors has been evaluated by radiologists. This indicates that our model may offer an alternative solution in scenarios where specific MR modalities of patients are unavailable. Extensive experiments further demonstrate the versatility of our method, providing unique insights into medical image translation.

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Publication type Article: Journal article

Document type Scientific Article

Corresponding Author

Keywords Disentangled Representations ; Hierarchical Granularity ; Medical Image Synthesis ; Structure Preservation; Segmentation

ISSN (print) / ISBN 1361-8415

e-ISSN 1361-8415

Journal Medical Image Analysis

Quellenangaben Volume: 100, Article Number: 103390

Publisher Elsevier

Publishing Place Radarweg 29, 1043 Nx Amsterdam, Netherlands

Non-patent literature Publications

Reviewing status Peer reviewed

Institute(s) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)

Grants National Natural Science Foundation of China
National Key Research and Development Program of China