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.

翻译：随着技术发展与健康意识提升，疾病无症状体征的早期识别能力不断增强。脑肿瘤因其潜在致命性，早期检测与治疗至关重要。尽管计算机辅助技术已逐步克服疾病诊断中的诸多局限，但脑肿瘤分割仍是具有挑战性的过程，尤其在涉及多模态数据时更为显著。该困难主要源于训练数据及相应标注的匮乏导致模型训练效果不佳。本研究探讨了利用深度伪造图像生成技术实现高效脑肿瘤分割的可行性。具体而言，采用生成对抗网络进行图像到图像的转换以扩充数据集，随后基于U-Net架构的卷积神经网络使用深度伪造图像进行分割训练。将该方法在四个公开数据集上的分割结果与金标准进行对比，实验表明，所提方法在图像分割质量评价指标上展现出更优性能，有望为数据稀缺场景下的模型训练提供有效辅助。