Deepfake Image Generation for Improved Brain Tumor Segmentation

Abstract

As the world progresses in technology and health, awareness of disease by revealing asymptomatic signs improves. It is important to detect and treat tumors in early stage as it can be life-threatening. Computer-aided technologies are used to overcome lingering limitations facing disease diagnosis, while brain tumor segmentation remains a difficult process, especially when multi-modality data is involved. This is mainly attributed to ineffective training due to lack of data and corresponding labelling. This work investigates the feasibility of employing deep-fake image generation for effective brain tumor segmentation. To this end, a Generative Adversarial Network was used for image-to-image translation for increasing dataset size, followed by image segmentation using a U-Net-based convolutional neural network trained with deepfake images. Performance of the proposed approach is compared with ground truth of four publicly available datasets. Results show improved performance in terms of image segmentation quality metrics, and could potentially assist when training with limited data.

arXiv comment6 pages, 8 figures, 2 tables, conference paper

References in corpus (9)

• Adam: A Method for Stochastic Optimization
• A Survey on Deep Learning in Medical Image Analysis
• Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations
• Transfusion: Understanding Transfer Learning for Medical Imaging
• TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation
• DeepSeg: Deep Neural Network Framework for Automatic Brain Tumor Segmentation using Magnetic Resonance FLAIR Images
• Assessment of texture measures susceptibility to noise in conventional and contrast enhanced computed tomography lung tumour images
• Medical Image Synthesis with Context-Aware Generative Adversarial Networks
• Susceptibility of texture measures to noise: an application to lung tumor CT images