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TECHNOLOGY AND CODE article
Front. Microbiol.
Sec. Systems Microbiology
Volume 15 - 2024 |
doi: 10.3389/fmicb.2024.1453870
This article is part of the Research Topic Artificial Intelligence in Pathogenic Microorganism Research View all articles
Lesion Region Inpainting: An Approach for Pseudo-Healthy Image Synthesis in Intracranial Infection Imaging
Provisionally accepted- 1 School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China
- 2 School of Big Data and Intelligent Engineering, Chongqing University of International Business and Economics, Chongqing, China
- 3 College of Computer Science and Engineering, Chongqing University of Technology, Chongqing, China
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.
Keywords: Pseudo-Healthy Image Synthesis, Generative Adversarial Networks, Intracranial infection, Data augmentation, Contextual Residual Attention Module Lesion Inpainting for Pseudo-Healthy Synthesis
Received: 24 Jun 2024; Accepted: 05 Aug 2024.
Copyright: © 2024 Liu, Xiang, Lan, Li, Xiao and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Xiaojuan Liu, School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China
Libin Lan, College of Computer Science and Engineering, Chongqing University of Technology, Chongqing, 400054, China
Chuan Li, School of Big Data and Intelligent Engineering, Chongqing University of International Business and Economics, Chongqing, China
Hanguang Xiao, School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China
Zhi Liu, School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China
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