# Predicting Post-Liposuction Body Shape Using RGB Image-to-Image Translation
## 3 Materials and Methods
These conditions were designed to ensure image consistency and allow accurate comparison of pre- and postoperative body shapes without distortion from lighting variations. All experiments were implemented in Python 3.9.7 using PyTorch 1.13.1 and torchvision 0.14.1. Model training was performed with CUDA 11.4 on an NVIDIA RTX 2060 GPU (NVIDIA Corporation, Santa Clara, CA, USA). Body mass index (BMI) data for the patients were also collected. **(Data Augmentation)** As only 230 image pairs were available, data augmentation was necessary to obtain meaningful results for analysis [22; 23]. Surgical procedures such as double-eyelid surgery and orbital decompression involve a clear and consistent pattern of alteration in appearance. By contrast, although liposuction always results in weight loss, determining whether a specific image depicts pre-liposuction or post-liposuction appearance is impossible. Specifically, for slim people undergoing liposuction [24], the amount of fat removable is relatively limited, whereas for heavier individuals, removing too much in a single session could be risky [25]. Thus, images of certain individuals after liposuction could depict more fat than images of other individuals before liposuction. Therefore, conventional GAN-based data augmentation, which relies on discrete categorical labels, could not be performed. Instead, data augmentation was performed using continuous cGAN (CcGAN) [25] based on BMI [26; 27], a continuous variable that reflects overall bodycharacteristics and is commonly used to evaluate obesity. Figure 2 presents side-by-side examples of actual patient images and augmented images generated by the CcGAN. Each pair includes both real and synthetic pre- and post-liposuction images, demonstrating that the generated images realistically reflect changes in BMI. **(Linear Regression)** Based on the BMI distributions before and after liposuction in Figure 3a, liposuction appears to shift the BMI to the left (i.e., toward small values). A paired t-test on the BMI values before and after liposuction showed a _p_-value of 1.0210 \(\times\) 10\({}^{-33}\), which indicates a significant difference. A Pearson correlation analysis was also performed to determine whether the BMI values before and after liposuction share a linear relationship [28]. Pearson correlation coefficients closer to 1 indicate more positive linear relationships [29]. The calculated Pearson correlation coefficient in this study was 0.9552, which indicates a highly linear relationship. Figure 3b presents a distribution graph visualizing this linear relationship. Therefore, we performed linear regression to predict the BMI after liposuction based on the BMI before liposuction. Based on these predictions, paired data samples (images before and after liposuction) were generated during data augmentation. **(CcGAN)** CcGAN is a generative architecture for conditional image generation based on continuous numerical values as auxiliary information instead of discrete class labels [30]. While existing cGANs generate images mainly based on categorical labels, they cannot directly utilize continuous data as labels. This limitation is addressed by CcGAN, which comprises a generator and a discriminator. Figure 4 illustrates the CcGAN architecture used in this study. Both the generator and discriminator in CcGAN are implemented as CNNs. The generator receives a noise vector and embedded BMI value, processed through a series of convolutional layers and upsampling blocks. Specifically, the generator consists of five transposed convolutional layers with filter sizes of 512, 256, 128, 64, and 3, respectively. Figure 2: Examples of real and CcGAN-generated images before and after liposuction This figure presents side-by-side comparisons of actual patient images (Original) and CcGAN-generated synthetic images (Augmented) before and after liposuction. Each row corresponds to one patient, with pre- and postoperative images shown for both real and generated cases.