# Predicting Post-Liposuction Body Shape Using RGB Image-to-Image Translation
## 5 Discussion and Conclusions  
With the continued development of deep learning, I2I has progressively gained importance in the medical field, with several excellent algorithms being developed. While some methods utilize medical data, most techniques involve using CT or X-ray data, which limits their accessibility to the general public [15, 16]. This study introduced a model to predict post-liposuction appearance using RGB images, which can be easily captured by potential patients themselves. The proposed model can predict how the body shape will change after liposuction based on the subject's body measurements. This enables intuitive predictions, which would not be possible when predicting the results for one individual based on those of other people. To the best of our knowledge, this study represents the first attempt at predicting post-liposuction appearance using body shape data. Despite being designed around liposuction, the proposed technique is expected to be useful for predicting changes caused by various other types of surgery that alter appearance and body shape. However, as few studies have sought to exploit such possibilities thus far, active research should be conducted on postoperative prediction using RGB images. Our method combines data augmentation using a Continuous Conditional GAN (CcGAN) with a Pix2 Pix-based image-to-image translation model. The effectiveness of this approach was verified through quantitative evaluation using four metrics: BMI MSE loss, LPIPS, SSIM, and PSNR. Our proposed model, which integrates both L1 and perceptual loss functions with CcGAN-based augmentation, achieved the lowest BMI MSE loss (0.0057), \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **BMI MSELoss** & **LPIPS** & **SSIM** & **PSNR** \\ \hline L1 + Augmentation & 0.0050 & 0.2861 & 0.7162 & 38.95 \\ \hline Perceptual + Augmentation & 0.0118 & 0.2820 & 0.7095 & 37.10 \\ \hline L1 + Perceptual & 0.0060 & 0.3145 & 0.7053 & 38.87 \\ \hline L1 + Perceptual + Augmentation (ours) & 0.0057 & 0.2709 & 0.7814 & 40.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative comparison of different Pix2 Pix-based loss function configurations for predicting post-liposuction images. The table reports BMI MSE loss, LPIPS, SSIM, and PSNR values for models using L1 Loss, perceptual loss, and a combined loss, with and without data augmentation using CcGAN. Our proposed model (L1 + Perceptual + Augmentation) achieved the lowest BMI MSE loss (0.0057), lowest LPIPS (0.2709), highest SSIM (0.7814), and highest PSNR (40.34), demonstrating superior structural accuracy, perceptual similarity, and image clarity. These results confirm that combining L1 and perceptual loss with CcGAN-based augmentation enhances both quantitative and visual prediction performance. lowest LPIPS (0.2709), highest SSIM (0.7814), and highest PSNR (40.34) among all compared methods (Table 3). These results indicate that the proposed model generates images that are not only quantitatively consistent with BMI predictions but also visually realistic and structurally faithful. Notwithstanding its benefits, the proposed method has certain limitations as well. For instance, the BMI predictor used to evaluate the CeGAN was based on ResNet34, and the model with the lowest error was used for data augmentation. Thus, the selection of the optimal model was entirely dependent on the BMI predictor's evaluation. As described in Section 3, blurring was frequently observed in the abdominal region, which was primarily attributed to the inconsistency in arm positioning during image acquisition. Although the images were captured from a fixed distance and camera height, no clear instructions were provided regarding the angle, position, or spacing of the arms. As a result, each subject adopted different poses--some with arms naturally hanging close to the body and others with arms slightly spread apart.