# Image Translation-Based Unsupervised Cross-Modality Domain Adaptation for Medical Image Segmentation

arXiv: https://arxiv.org/abs/2502.15193 · HTML: https://mlsanity.com/paper/2502.15193/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 7 references that are in the map.
- First posted on arXiv 21 Feb 2025.

## Builds on (most cited first, 3 of 3)

- [COSMOS: Cross-Modality Unsupervised Domain Adaptation for 3D Medical Image Segmentation based on Target-aware Domain Translation and Iterative Self-Training](https://mlsanity.com/paper/2203.16557/) (2022)
- [Unsupervised Domain Adaptation in Semantic Segmentation Based on Pixel Alignment and Self-Training](https://mlsanity.com/paper/2109.14219/) (2021)
- [Self-Training Based Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation](https://mlsanity.com/paper/2109.10674/) (2021)

## Built on by (most cited first, 1 of 1)

- [crossMoDA Challenge: Evolution of Cross-Modality Domain Adaptation Techniques for Vestibular Schwannoma and Cochlea Segmentation from 2021 to 2023](https://mlsanity.com/paper/2506.12006/) (2025)

## Abstract (arXiv)

Supervised deep learning usually faces more challenges in medical images than in natural images. Since annotations in medical images require the expertise of doctors and are more time-consuming and expensive. Thus, some researchers turn to unsupervised learning methods, which usually face inevitable performance drops. In addition, medical images may have been acquired at different medical centers with different scanners and under different image acquisition protocols, so the modalities of the medical images are often inconsistent. This modality difference (domain shift) also reduces the applicability of deep learning methods. In this regard, we propose an unsupervised crossmodality domain adaptation method based on image translation by transforming the source modality image with annotation into the unannotated target modality and using its annotation to achieve supervised learning of the target modality. In addition, the subtle differences between translated pseudo images and real images are overcome by self-training methods to further improve the task performance of deep learning. The proposed method showed mean Dice Similarity Coefficient (DSC) and Average Symmetric Surface Distance (ASSD) of 0.8351 ± 0.1152 and 1.6712 ± 2.1948 for vestibular schwannoma (VS), 0.8098 ± 0.0233 and 0.2317 ± 0.1577 for cochlea on the VS and cochlea segmentation task of the Cross-Modality Domain Adaptation (crossMoDA 2022) challenge validation phase leaderboard.