Convolutional neural networks are good at recognizing disease symptoms in medical scans of patients who were injected with iodine-based dye, known as radiocontrast, that makes their organs more visible. But some patients can’t take the dye. Now synthetic scans from a GAN are helping CNNs learn to analyze undyed images.

What’s new: Researchers from the U.S. National Institutes of Health and University of Wisconsin developed a GAN that generates labeled, undyed computerized tomography (CT) images of lesions on kidneys, spleens, and livers. They added these images to real-world training data to improve performance of a segmentation model that marks lesions in diagnostic scans.

How it works: The work is based on CycleGAN and the DeepLesion dataset of CTs. CycleGAN has been used to turn pictures of horses into pictures of zebras without needing to match particular zebra and horse pics. This work takes advantage of that capability to map between dyed and undyed CTs.

  • The authors used a CNN to sort DeepLesion into images of dyed and undyed patients. They trained the GAN on a portion of the dataset, including both dyed and undyed CTs, and generated fake undyed images.
  • Using a mix of CycleGAN output and natural images, they trained a U-Net segmentation model to isolate lesions, organs, and other areas of interest.
  • To compare their approach with alternatives, they trained separate U-Nets on variations of DeepLesion: dyed images in which the dye had been artificially lightened, images that had been augmented via techniques like rotation and cropping, and the dataset without alterations.

Results: Tested on undyed, real-world CT scans, the U-Net trained on the combination of CycleGAN output and natural images outperformed the others. It was best at identifying lesions on kidneys, achieving a 57 percent improvement over the next-best model. With lesions on spleens, the spread was 4 percent; on livers, 3 percent. In estimating lesion volume, it achieved  an average error of 0.178, compared to the next-highest score of 0.254. Tested on the remainder of the dyed DeepLesion images, all four U-Nets isolated lesions roughly equally well.

Behind the news: The researchers behind this model have used it to improve screening for dangerous levels of liver fat and to identify patients with high risk of metabolic syndrome, a precursor to heart disease, diabetes, and stroke.

Why it matters: Medical data can be hard to come by and labeled medical data even more so. GANs are making it easier and less expensive to create large, annotated datasets for training AI diagnostic tools.

We’re thinking: Medical AI is just beginning to be recognized by key healthcare players in the U.S. Clever uses of CycleGAN and other architectures could accelerate the process.


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