Image Translation for Medical Image Generation -- Ischemic Stroke Lesions

Abstract

Deep learning-based automated disease detection and segmentation algorithms promise to accelerate and improve many clinical processes. However, such algorithms require vast amounts of annotated training data, which are typically not available in a medical context, e.g., due to data privacy concerns, legal obstructions, and non-uniform data formats. Synthetic databases of annotated pathologies could provide the required amounts of training data. Here, we demonstrate with the example of ischemic stroke that a significant improvement in lesion segmentation is feasible using deep learning-based data augmentation. To this end, we train different image-to-image translation models to synthesize diffusionweighted magnetic resonance images (DWIs) of brain volumes with and without stroke lesions from semantic segmentation maps. In addition, we train a generative adversarial network to generate synthetic lesion masks. Subsequently, we combine these two components to build a large database of synthetic stroke DWIs. The performance of the various generative models is evaluated using a U-Net which is trained to segment stroke lesions on a clinical test set. We compare the results to human expert inter-reader scores. For the model with the best performance, we report a maximum Dice score of 82.6%, which significantly outperforms the model trained on the clinical images alone (74.8%), and also the inter-reader Dice score of two human readers of 76.9%. Moreover, we show that for a very limited database of only 10 or 50 clinical cases, synthetic data can be used to pre-train the segmentation algorithms, which ultimately yields an improvement by a factor of as high as 8 compared to a setting where no synthetic data is used.

Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review

“…This negligible difference indicates the quality of the generated images. By the same token, Platscher et al [44] propose using a two-step image translation approach to generate MRI images with ischemic stroke lesion masks. The first step consists of generating synthetic stroke lesion masks using a WGAN.…”

“…In the case of segmentation and translation, the architectures that have shown the most promise include Pix2Pix, CycleGAN, and SPADE, all of which have proven their potential for conditional generation and cross-modal translation. Platscher et al [44] conducted a comparative study of these three architectures, demonstrating their capacity to generate high-quality images suitable for medical image segmentation and translation tasks (improvement of 9.1% in Dice score). These architectures can significantly reduce the need for manual annotation of medical images and thus significantly reduce the time and cost required for data annotation.…”