Generator Versus Segmentor: Pseudo-healthy Synthesis
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Pseudo-healthy synthesis is defined as synthesizing a subject-specific 'healthy' image from a pathological one, with applications ranging from segmentation to anomaly detection. In recent years, the existing GAN-based methods proposed for pseudo-healthy synthesis aim to eliminate the global differences between synthetic and healthy images. In this paper, we discuss the problems of these approaches, which are the style transfer and artifacts respectively. To address these problems, we consider the local differences between the lesions and normal tissue. To achieve this, we propose an adversarial training regime that alternatively trains a generator and a segmentor. The segmentor is trained to distinguish the synthetic lesions (i.e. the region in synthetic images corresponding to the lesions in the pathological ones) from the normal tissue, while the generator is trained to deceive the segmentor by transforming lesion regions into lesion-free-like ones and preserve the normal tissue at the same time. Qualitative and quantitative experimental results on public datasets BraTS and LiTS demonstrate that the proposed method outperforms state-of-the-art methods by preserving style and removing the artifacts. Our implementation is publicly available at https://github.com/Au3C2/Generator-Versus-Segmentor
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