Lesion region inpainting: an approach for pseudo-healthy image synthesis in intracranial infection imaging

The synthesis of pseudo-healthy images, involving the generation of healthy counterparts for pathological images, is crucial for data augmentation, clinical disease diagnosis, and understanding pathology-induced changes. Recently, Generative Adversarial Networks (GANs) have shown substantial promise in this domain. However, the heterogeneity of intracranial infection symptoms caused by various infections complicates the model's ability to accurately differentiate between pathological and healthy regions, leading to the loss of critical information in healthy areas and impairing the precise preservation of the subject's identity. Moreover, for images with extensive lesion areas, the pseudo-healthy images generated by these methods often lack distinct organ and tissue structures. To address these challenges, we propose a three-stage method (localization, inpainting, synthesis) that achieves nearly perfect preservation of the subject's identity through precise pseudo-healthy synthesis of the lesion region and its surroundings. The process begins with a Segmentor, which identifies the lesion areas and differentiates them from healthy regions. Subsequently, a Vague-Filler fills the lesion areas to construct a healthy outline, thereby preventing structural loss in cases of extensive lesions. Finally, leveraging this healthy outline, a Generative Adversarial Network integrated with a contextual residual attention module generates a more realistic and clearer image. Our method was validated through extensive experiments across different modalities within the BraTS2021 dataset, achieving a healthiness score of 0.957. The visual quality of the generated images markedly exceeded those produced by competing methods, with enhanced capabilities in repairing large lesion areas. Further testing on the COVID-19-20 dataset showed that our model could effectively partially reconstruct images of other organs.