## 5 Experiments and Results
### Display of Results and Analysis Using Pix2 Pix and Pix2 PixHD with Masks  
**Pix2 Pix with Masks:** In this study, we prepared datasets to address the challenge of using masks as a single-channel input for generating new X-ray images. Our approach assigned a specific pixel value to each mask, uniquely representing different object categories. This method is particularly advantageous for X-ray imaging, where differentiating between elements requires a nuanced distinction. Each category within our dataset [45] was allocated a unique pixel value, facilitating a clearer differentiation among categories. An example of these masks is illustrated in Figure 5. To accommodate this labelling strategy, the Pix2 Pix model was adapted; its generator output layer was modified to produce images where each pixel corresponded to the mask values, allowing the model to not only generate visual representations of objects but also classify them implicitly based on the mask's pixel values. Additionally, the discriminator was fine-tuned to assess both the authenticity of the generated images and the accuracy of object representation as per the mask labels, enhancing the model's ability to effectively distinguish between categories. **Pix2 PixHD with masks:** Following the Pix2 Pix experiment, we applied the same dataset and masking technique as shown in Figure 5 to explore the performance of Pix2 PixHD, a model designed for high-resolution image synthesis. We also used masks. This approach let us directly compare how well Pix2 PixHD and the Pix2 Pix model worked at generating new X-ray images. Given the advanced architecture of Pix2 PixHD, which is capable of handling greater detail and producing higher-fidelity images, we anticipated an  
Figure 5: Examples of mask images for different object categories. improvement in the model's ability to generate visually accurate representations of objects, classify them based on the pixel values of the mask, and generate correct images. The experiments conducted with the Pix2 Pix and Pix2 PixHD models, utilising masks to categorize objects in X-ray images, yielded significant findings. The visual results from the Pix2 Pix model, as shown in Figure 6, did not meet the desired standards in terms of image quality and realism. Despite capturing the essence of the object categories represented by the masks, the generated images fell short of expectations with a PSNR of 11.50, SSIM of 0.41, LPIPS of 0.50, FID score of 166.44, and an FSIM score of 0.35. These results indicated the potential for improvement in enhancing the fidelity and realism of the generated images. In response to these findings, we conducted another experiment using Pix2 PixHD to explore alternative approaches for generating more detailed and high-quality images. The Pix2 PixHD model demonstrated markedly improved performance, achieving a PSNR of 16.43, SSIM of 0.60, LPIPS of 0.19, FID score of 35.36, and an FSIM of 0.46. This essential enhancement in the metric scores reflected Pix2 PixHD's advanced capabilities in handling high-resolution and detailed image synthesis. The lower FID and high FSIM scores suggested a closer similarity of the generated images to the real X-ray images, demonstrating Pix2 PixHD's superior ability to produce detailed and visually accurate representations of the objects. Moreover, the visual results, as shown in Figure 6, closely approached the ground truth, confirming that Pix2 PixHD was able to generate images remarkably similar to real X-ray images. The significant difference in FID and FSIM scores between Pix2 Pix and  
Figure 6: Detailed comparison of image synthesis using single-channel mask inputs across models—column 1: Pix2 Pix output, column 2: Pix2 PixHD output, column 3: corresponding ground-truth images. Pix2 PixHD highlights the importance of choosing the right model for the image synthesis task, particularly when high fidelity is crucial for creating detailed outputs from masks as inputs.