mlsanityData to 7 Oct 2026

Anatomical feature-prioritized loss for enhanced MR to CT translation

arXiv 2410.10328 ↗First posted 14 Oct 2024eess.IV cs.CVcited by 4

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Abstract (arXiv)

In medical image synthesis, the precision of localized structural details is crucial, particularly when addressing specific clinical requirements such as the identification and measurement of fine structures. Traditional methods for image translation and synthesis are generally optimized for global image reconstruction but often fall short in providing the finesse required for detailed local analysis. This study represents a step toward addressing this challenge by introducing a novel anatomical feature-prioritized (AFP) loss function into the synthesis process. This method enhances reconstruction by focusing on clinically significant structures, utilizing features from a pre-trained model designed for a specific downstream task, such as the segmentation of particular anatomical regions. The AFP loss function can replace or complement global reconstruction methods, ensuring a balanced emphasis on both global image fidelity and local structural details. Various implementations of this loss function are explored, including its integration into different synthesis networks such as GAN-based and CNN-based models. Our approach is applied and evaluated in two contexts: lung MR to CT translation, focusing on high-quality reconstruction of bronchial structures, using a private dataset; and pelvis MR to CT synthesis, targeting the accurate representation of organs and muscles, utilizing a public dataset from the Synthrad2023 challenge. We leverage embeddings from pre-trained segmentation models specific to these anatomical regions to demonstrate the capability of the AFP loss to prioritize and accurately reconstruct essential features. This tailored approach shows promising potential for enhancing the specificity and practicality of medical image synthesis in clinical applications.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 30 of its 49 references that are in the map. First posted on arXiv 14 Oct 2024.

Builds on

30 of 49 references.

References · 30

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18 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025Deep Learning-Based Cross-Anatomy CT Synthesis Using Adapted nnResU-Net with Anatomical Feature Prioritized Losscited by 0
  2. 2026Mamba-driven MRI-to-CT Synthesis for MRI-only Radiotherapy Planningcited by 0
  3. 2026VS-DDPM: Efficient Low-Cost Diffusion Model for Medical Modality Translationcited by 0
  4. 2026Anatomical and Physical Supervision for CT-less PET Attenuation Correction: BIC-MAC 2026 Challengecited by 0