# ColorMedGAN: A Semantic Colorization Framework for Medical Images
## 5 Conclusions  
The task of colorizing medical images is challenging due to the limited availability of paired data. In this paper, our main contribution is to propose a self-supervised framework that aims to generate high-quality color images that are semantically informative and preserve texture details. We demonstrate how to construct a dataset with a colorization architecture, remove the image background to eliminate the effect of the background on the generated color and add a modal transformation module to the segmentation branch on the symmetric architecture of CycleGAN to enable the branch to function in a loop. The CycleGAN model shows good convergence and generalization on MR images and can map the color space of cryosection onto the data domain of cross-modal MR images. Our results demonstrate that our method generates high-fidelity and high-quality color images, despite not relying on paired data. This framework provides a promising approach for colorizing medical images and can facilitate the interpretation of medical images by non-specialists.  
Conceptualization, S.C. and Y.Q.; methodology, S.C.; software, S.C.; validation, S.C.; formal analysis, X.S.; investigation, Y.Y.; resources, H.T.; data curation, J.T.; writing--original draft preparation, S.C. and N.X.; writing--review and editing, S.C. and X.S.; visualization, N.X.; project administration, Y.Q.; funding acquisition, Y.Q. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key R&D Program of China (2018 YFC2002500), Guangdong Basic and Applied Basic Research Foundation (2021A1515011999), Guangdong Province Big Data Innovation Engineering Technology Research Center, \"OutstandingPuture\" Data Scientist Incubation Project of Jinan University, and in part by the Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Information under Grant 2021B1212040007.  
Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. The Visible Korean Human can be found here: [[https://archive.org/details/VisibleKoreanHumanSmallImages/](https://archive.org/details/VisibleKoreanHumanSmallImages/)]([https://archive.org/details/VisibleKoreanHumanSmallImages/](https://archive.org/details/VisibleKoreanHumanSmallImages/)), accessed on 29 January 2023; The Visible Human Project can be found here: [[https://www.nlm.nih.gov/databases/download/vhp.html](https://www.nlm.nih.gov/databases/download/vhp.html)]([https://www.nlm.nih.gov/databases/download/vhp.html](https://www.nlm.nih.gov/databases/download/vhp.html)), accessed on 29 January 2023; The Oasis Dataset can be found here: [[http://www.oasis-brains.org/](http://www.oasis-brains.org/)]([http://www.oasis-brains.org/](http://www.oasis-brains.org/)), accessed on 29 January 2023.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **SSMI \(\uparrow\)** & **PSNR \(\uparrow\)** & **FID \(\downarrow\)** \\ \hline colorCycleGAN & 0.69 & 19.86 & 163.65 \\ CycleGAN & 0.28 & 11.01 & 266.58 \\ [PERSON]’s & 0.89 & 32.45 & 104.72 \\ ChromaGAN & 0.89 & 27.48 & 125.65 \\
**Ours** & **0.90** & **33.22** & **84.81** \\ \hline \hline \end{tabular} Bold fonts indicate better results.  
\end{table}
Table 5: Comparison of different models.  
**Acknowledgments:** We are grateful for the dataset and annotations provided by the Imaging Department of the First Affiliated Hospital of Jinan University. Furthermore, thanks to the anonymous reviewers for their insightful comments, which improved the quality of this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.