In this work, we conducted two re-implementations: one for Wasserstein Generative Adversarial Network (WGAN) using conv2d layers, and another for 3D-WGAN employing conv3d layers. Notice that this work is intended to provide a quick overview of 2D-GAN and 3D-GAN, not to implement state-of-the-art models or similar advanced approaches.
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2D-GAN models are effective at generating fake images, but they are not as proficient as 3D-GANs in handling the sequential nature of CT scans. While 2D-GANs are capable of producing synthetic images, the power of 3D-GANs lies in their ability to preserve spatial relationships within volumetric CT data, resulting in more accurate and realistic image synthesis.
| CT-Fake-Image-Samples from 2DGAN |
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A single patient's CT scan consists of approximately 64 slices, where each slice represents a cross-sectional image of the body taken at different positions. These slices collectively form a volumetric data set, providing a comprehensive 3D view of the patient's anatomy, which is crucial for precise medical diagnosis and treatment planning.
3D-GANs are equipped with 3D convolutional layers, enabling them to handle and generate synthetic images as a sequence of CT scans properly. This capability is crucial, considering that each patient's CT scan typically consists of a sequence of slices taken at different positions, representing a three-dimensional volume.
By leveraging conv3d layers, 3D-GANs can capture the spatial and temporal relationships between adjacent slices in a CT scan, resulting in more accurate and realistic synthetic CT volumes. This is particularly valuable in medical imaging, where a comprehensive understanding of the 3D structure is essential for precise diagnosis, treatment planning, and analysis.
In summary, 3D-GANs are well-suited for generating synthetic CT scans due to their ability to process and maintain the spatial continuity of volumetric data, making them a valuable tool for medical image synthesis.
| CT-Fake-Image-Samples from 3DGAN |
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