# Cross-Domain Medical Image Translation by Shared Latent Gaussian Mixture Model

arXiv: https://arxiv.org/abs/2007.07230 · HTML: https://mlsanity.com/paper/2007.07230/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2020.

## Built on by (most cited first, 12 of 12)

- [BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural Networks](https://mlsanity.com/paper/2207.06873/) (2022)
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- [Anatomically constrained CT image translation for heterogeneous blood vessel segmentation](https://mlsanity.com/paper/2210.01713/) (2022)
- [Multi-Task, Multi-Domain Deep Segmentation with Shared Representations and Contrastive Regularization for Sparse Pediatric Datasets](https://mlsanity.com/paper/2105.10310/) (2021)
- [Generalizable multi-task, multi-domain deep segmentation of sparse pediatric imaging datasets via multi-scale contrastive regularization and multi-joint anatomical priors](https://mlsanity.com/paper/2207.13502/) (2022)
- [A Domain Translation Framework with an Adversarial Denoising Diffusion Model to Generate Synthetic Datasets of Echocardiography Images](https://mlsanity.com/paper/2403.04612/) (2024)
- [CT to PET Translation: A Large-scale Dataset and Domain-Knowledge-Guided Diffusion Approach](https://mlsanity.com/paper/2410.21932/) (2024)
- [Learning Gaussian Representation for Eye Fixation Prediction](https://mlsanity.com/paper/2403.14821/) (2024)
- [A Strictly Bounded Deep Network for Unpaired Cyclic Translation of Medical Images](https://mlsanity.com/paper/2311.02480/) (2023)
- [Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision](https://mlsanity.com/paper/2607.02051/) (2026)

## Abstract (arXiv)

Current deep learning based segmentation models often generalize poorly between domains due to insufficient training data. In real-world clinical applications, cross-domain image analysis tools are in high demand since medical images from different domains are often needed to achieve a precise diagnosis. An important example in radiology is generalizing from non-contrast CT to contrast enhanced CTs. Contrast enhanced CT scans at different phases are used to enhance certain pathologies or organs. Many existing cross-domain image-to-image translation models have been shown to improve cross-domain segmentation of large organs. However, such models lack the ability to preserve fine structures during the translation process, which is significant for many clinical applications, such as segmenting small calcified plaques in the aorta and pelvic arteries. In order to preserve fine structures during medical image translation, we propose a patch-based model using shared latent variables from a Gaussian mixture model. We compare our image translation framework to several state-of-the-art methods on cross-domain image translation and show our model does a better job preserving fine structures. The superior performance of our model is verified by performing two tasks with the translated images - detection and segmentation of aortic plaques and pancreas segmentation. We expect the utility of our framework will extend to other problems beyond segmentation due to the improved quality of the generated images and enhanced ability to preserve small structures.