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This study investigates the foundational characteristics of image-to-image translation networks, specifically examining their suitability and transferability within the context of routine clinical environments, despite achieving high levels of performance, as indicated by a Structural Similarity Index (SSIM) exceeding 85%. The evaluation study was conducted using data from 794 patients diagnosed with Prostate cancer (PCa). To synthesize MRI from Ultrasound (US) images, we employed five widely recognized image-to-image translation networks in medical imaging: 2D-Pix2Pix, 2D-CycleGAN, 3D-CycleGAN, 3D-UNET, and 3D-AutoEncoder. For quantitative assessment, we report four prevalent evaluation metrics: Mean Absolute Error (MAE), Mean Square Error (MSE), Structural Similarity Index (SSIM), and Peak Signal to Noise Ratio (PSNR). Moreover, a complementary analysis employing Radiomic features (RF) via Spearman correlation coefficient was conducted to investigate, for the first time, whether networks achieving high performance (SSIM > 85%) could identify low-level RFs. The RF analysis showed 75 features out of 186 RFs were discovered via just 2D-Pix2Pix algorithm while half of RFs were lost in the translation process. Finally, a detailed qualitative assessment by five medical doctors indicated a lack of low-level feature discovery in image-to-image translation tasks. This study indicates current image-to-image translation networks, even with a high performance (SSIM > 0.85), don’t guarantee the discovery of low-level information which is essential for the integration of synthesized MRI data into regular clinical practice.

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Do High-Performance Image-to-Image Translation Networks Enable the Discovery of Radiomic Features? Application to MRI Synthesis from Ultrasound in Prostate Cancer

  • 会议论文

  • 发表日期：

    2025年

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  • Mohammad R. Salmanpour,
  • Amin Mousavi,
  • Yixi Xu,
  • William B. Weeks,
  • Ilker Hacihaliloglu

摘要

This study investigates the foundational characteristics of image-to-image translation networks, specifically examining their suitability and transferability within the context of routine clinical environments, despite achieving high levels of performance, as indicated by a Structural Similarity Index (SSIM) exceeding 85%. The evaluation study was conducted using data from 794 patients diagnosed with Prostate cancer (PCa). To synthesize MRI from Ultrasound (US) images, we employed five widely recognized image-to-image translation networks in medical imaging: 2D-Pix2Pix, 2D-CycleGAN, 3D-CycleGAN, 3D-UNET, and 3D-AutoEncoder. For quantitative assessment, we report four prevalent evaluation metrics: Mean Absolute Error (MAE), Mean Square Error (MSE), Structural Similarity Index (SSIM), and Peak Signal to Noise Ratio (PSNR). Moreover, a complementary analysis employing Radiomic features (RF) via Spearman correlation coefficient was conducted to investigate, for the first time, whether networks achieving high performance (SSIM > 85%) could identify low-level RFs. The RF analysis showed 75 features out of 186 RFs were discovered via just 2D-Pix2Pix algorithm while half of RFs were lost in the translation process. Finally, a detailed qualitative assessment by five medical doctors indicated a lack of low-level feature discovery in image-to-image translation tasks. This study indicates current image-to-image translation networks, even with a high performance (SSIM > 0.85), don’t guarantee the discovery of low-level information which is essential for the integration of synthesized MRI data into regular clinical practice.