MedGAN: Medical Image Translation using GANs (1806.06397v2)

Abstract: Image-to-image translation is considered a new frontier in the field of medical image analysis, with numerous potential applications. However, a large portion of recent approaches offers individualized solutions based on specialized task-specific architectures or require refinement through non-end-to-end training. In this paper, we propose a new framework, named MedGAN, for medical image-to-image translation which operates on the image level in an end-to-end manner. MedGAN builds upon recent advances in the field of generative adversarial networks (GANs) by merging the adversarial framework with a new combination of non-adversarial losses. We utilize a discriminator network as a trainable feature extractor which penalizes the discrepancy between the translated medical images and the desired modalities. Moreover, style-transfer losses are utilized to match the textures and fine-structures of the desired target images to the translated images. Additionally, we present a new generator architecture, titled CasNet, which enhances the sharpness of the translated medical outputs through progressive refinement via encoder-decoder pairs. Without any application-specific modifications, we apply MedGAN on three different tasks: PET-CT translation, correction of MR motion artefacts and PET image denoising. Perceptual analysis by radiologists and quantitative evaluations illustrate that the MedGAN outperforms other existing translation approaches.

• The paper introduces an end-to-end GAN framework that integrates adversarial, perceptual, and style-transfer losses for robust medical image translation.
• The paper implements a CasNet generator with chained encoder-decoder pairs to progressively refine and enhance image details.
• The paper demonstrates superior performance on PET-CT translation, MR motion correction, and PET denoising compared to methods like pix2pix and Fila-sGAN.

Overview of MedGAN: Medical Image Translation using GANs

The paper "MedGAN: Medical Image Translation using GANs" introduces a novel framework aimed at enhancing medical image analysis through the use of Generative Adversarial Networks (GANs). The approach aligns with the growing interest in leveraging GANs for various medical image processing tasks, such as modality translation, motion correction, and image denoising.

Key Contributions

1. End-to-End Framework: MedGAN offers an end-to-end architecture for image-to-image translation in medical contexts, overcoming limitations of task-specific models and enhancing general applicability without modification to specific medical domains.
2. Combination of Losses: The framework integrates adversarial losses with perceptual and style-transfer losses, allowing it to capture both high and low-frequency components of target images. The incorporation of these non-adversarial losses helps in refining global structure consistency and maintaining detail sharpness.
3. CasNet Generator Architecture: MedGAN employs the CasNet architecture, which chains encoder-decoder pairs to iteratively refine the output, ensuring high-resolution and detailed medical images. The utilization of multiple U-blocks in CasNet allows for progressive refinement of the generated images.
4. Robust Evaluation Across Tasks: The framework is tested across three distinct medical imaging tasks—PET to CT translation, MR motion correction, and PET image denoising—exhibiting superior qualitative and quantitative performance compared to existing methods such as pix2pix and Fila-sGAN.

Strong Numerical Outcomes

MedGAN demonstrates quantitatively superior outcomes in terms of metrics such as SSIM, PSNR, and MSE across different tasks, indicating enhanced image quality and fidelity compared to existing methods. The framework achieved the best scores in most metrics, highlighting its efficacy in producing medically relevant and consistent image translations.

Implications and Future Directions

MedGAN's applicability without task-specific modification significantly streamlines the workflow in medical imaging, potentially reducing the need for additional imaging procedures. This could have substantial practical implications, particularly in clinical settings where resources are constrained.

The inclusion of both perceptual and style-transfer losses might open pathways to explore more nuanced applications, including enhancing diagnostic accuracy by ensuring that synthetic images preserve essential diagnostic information. The robust perceptual quality of translations also points toward potential use in technical post-processing tasks, such as segmentation and organ volume calculations.

Future work might focus on expanding MedGAN to handle 3D volumetric data and multi-channel inputs, which are critical in advanced medical imaging applications. Additionally, exploring domain adaptation and unsupervised translation could further enhance its usability in diverse medical environments.

Conclusion

MedGAN, through its innovative design and integration of various losses, represents a significant progression in the field of medical image translation. Its success across multiple tasks without specific alterations underscores its versatility and potential impact on medical imaging workflows. Continued research and development in this domain promise to extend the capabilities of AI in transforming medical diagnostics and treatment planning processes.