Deepfake Image Generation for Improved Brain Tumor Segmentation

TL;DR

This paper explores using deepfake image generation via GANs to augment data for brain tumor segmentation, demonstrating improved segmentation performance with limited datasets.

Contribution

It introduces a novel approach combining deepfake image generation with U-Net segmentation to enhance brain tumor detection accuracy.

Findings

Improved segmentation quality metrics with deepfake-augmented data

Effective data augmentation for limited datasets

Potential to assist early diagnosis with limited labeled data

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.