# Predicting Post-Liposuction Body Shape Using RGB Image-to-Image Translation
## 4 Results
These examples illustrate the model’s ability to generate visually plausible postoperative predictions under realistic conditions. combination of L1 and perceptual loss results in more realistic and sharper predictions, better reflecting the structural and contour changes after liposuction. Figure 11: Prediction failure due to inconsistent arm position during data acquisition. This figure illustrates a failure case in which the predicted post-liposuction image (middle) fails to reflect morphological changes accurately, primarily because of inconsistent arm positioning between pre-(left) and postoperative (right) images. Although camera height and distance were fixed, the lack of explicit posture guidelines caused large variations in arm placement across individuals. This led to an ambiguous boundary between the arms and abdomen, introducing blurring artifacts and reducing the model’s ability to learn precise contours in the abdominal region. Figure 12: Prediction failure case in a low-BMI patient with minimal postoperative change. From left to right: the first image shows the ground truth (GT) before liposuction, the second is the predicted image generated by the model, and the third is the ground truth (GT) after liposuction. In this case, the patient’s BMI changed only slightly (from 18.80 to 18.07), resulting in a minimal visual difference between the pre- and post-liposuction appearances. The predicted image closely resembles the preoperative image, failing to reflect the subtle morphological change. This example highlights the model’s current limitation in capturing slight body contour differences, particularly in patients with low BMI. Future improvements should focus on enhancing the model’s sensitivity to subtle variations and preserving fine-grained details to ensure accurate postoperative predictions in such cases. From left to right: the first image shows the ground truth (GT) before liposuction, the second is the predicted result after liposuction, and the third is the ground truth (GT) after liposuction. In this example, the model failed to capture the subtle morphological differences caused by minimal fat reduction. As a result, the predicted image closely resembles the preoperative state rather than the actual postoperative outcome. This highlights the model’s limitation in handling slight body contour changes, especially common in patients with low BMI. Enhancing the model’s sensitivity to such subtle variations remains an important area for future improvement. **(Quantitative Evaluation)** We conducted four types of quantitative evaluation. First, we evaluated the accuracy of the generated images using the BMI predictor, as described earlier. This was measured by the mean squared error (MSE) between the predicted BMI and the ground truth BMI. A lower BMI MSE loss indicates higher prediction accuracy and consistency between the generated image and the actual outcome. Second, the similarity between two given images was determined based on the Learned Perceptual Image Patch Similarity(LPIPS) metric, which can mimic human evaluation. Specifically, the LPIPS metric calculates the similarity between the features of two images using AlexNet [44] pretrained with ImageNet [33]; when linear weights are incorporated, the evaluation resembles subjective judgment based on human cognition [43]. Since LPIPS measures dissimilarity, a lower LPIPS value indicates greater perceptual similarity between the generated and ground truth images and is therefore considered better. Third, we adopted the Structural Similarity Index Measure (SSIM) [45], a widely used metric in medical image translation, to evaluate the structural fidelity of the generated images. SSIM measures the similarity in structural information, luminance, and contrast between two images. A higher SSIM value (closer to 1.0) indicates better preservation of spatial structure and local textures. Fourth, we used the Peak Signal-to-Noise Ratio (PSNR) [46] to evaluate image clarity.