# Anatomical feature-prioritized loss for enhanced MR to CT translation

arXiv: https://arxiv.org/abs/2410.10328 · HTML: https://mlsanity.com/paper/2410.10328/

## Key facts

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

## Builds on (most cited first, 30 of 30)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [TotalSegmentator: robust segmentation of 104 anatomical structures in CT images](https://mlsanity.com/paper/2208.05868/) (2022)
- [Diffusion Models for Medical Image Analysis: A Comprehensive Survey](https://mlsanity.com/paper/2211.07804/) (2022)
- [Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation](https://mlsanity.com/paper/1908.10454/) (2019)
- [Metrics reloaded: Recommendations for image analysis validation](https://mlsanity.com/paper/2206.01653/) (2022)
- [EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis](https://mlsanity.com/paper/1612.07919/) (2016)
- [MedGAN: Medical Image Translation using GANs](https://mlsanity.com/paper/1806.06397/) (2018)
- [Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks](https://mlsanity.com/paper/1807.10225/) (2018)
- [Deep MR to CT Synthesis using Unpaired Data](https://mlsanity.com/paper/1708.01155/) (2017)
- [Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy](https://mlsanity.com/paper/1809.04430/) (2018)
- [Generative AI for Medical Imaging: extending the MONAI Framework](https://mlsanity.com/paper/2307.15208/) (2023)
- [SynthRAD2023 Grand Challenge dataset: generating synthetic CT for radiotherapy](https://mlsanity.com/paper/2303.16320/) (2023)
- [Make-A-Volume: Leveraging Latent Diffusion Models for Cross-Modality 3D Brain MRI Synthesis](https://mlsanity.com/paper/2307.10094/) (2023)
- [Can segmentation models be trained with fully synthetically generated data?](https://mlsanity.com/paper/2209.08256/) (2022)
- [NaviAirway: a Bronchiole-sensitive Deep Learning-based Airway Segmentation Pipeline](https://mlsanity.com/paper/2203.04294/) (2022)
- [Unsupervised learning for cross-domain medical image synthesis using deformation invariant cycle consistency networks](https://mlsanity.com/paper/1808.03944/) (2018)
- [Structural Attention: Rethinking Transformer for Unpaired Medical Image Synthesis](https://mlsanity.com/paper/2406.18967/) (2024)
- [SA-GAN: Structure-Aware GAN for Organ-Preserving Synthetic CT Generation](https://mlsanity.com/paper/2105.07044/) (2021)
- [A 3D generative model of pathological multi-modal MR images and segmentations](https://mlsanity.com/paper/2311.04552/) (2023)
- [Region of Interest focused MRI to Synthetic CT Translation using Regression and Classification Multi-task Network](https://mlsanity.com/paper/2203.16288/) (2022)
- [HiFi-Syn: Hierarchical Granularity Discrimination for High-Fidelity Synthesis of MR Images with Structure Preservation](https://mlsanity.com/paper/2311.12461/) (2023)
- [CT evaluation of 2D and 3D holistic deep learning methods for the volumetric segmentation of airway lesions](https://mlsanity.com/paper/2403.08042/) (2024)

## Built on by (most cited first, 4 of 4)

- [Deep Learning-Based Cross-Anatomy CT Synthesis Using Adapted nnResU-Net with Anatomical Feature Prioritized Loss](https://mlsanity.com/paper/2509.22394/) (2025)
- [Mamba-driven MRI-to-CT Synthesis for MRI-only Radiotherapy Planning](https://mlsanity.com/paper/2603.23295/) (2026)
- [VS-DDPM: Efficient Low-Cost Diffusion Model for Medical Modality Translation](https://mlsanity.com/paper/2604.22942/) (2026)
- [Anatomical and Physical Supervision for CT-less PET Attenuation Correction: BIC-MAC 2026 Challenge](https://mlsanity.com/paper/2608.15721/) (2026)

## 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.