Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis
- URL: http://arxiv.org/abs/2203.15347v1
- Date: Tue, 29 Mar 2022 08:41:17 GMT
- Title: Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis
- Authors: Yunlong Zhang and Xin Lin and Yihong Zhuang and LiyanSun and Yue Huang
and Xinghao Ding and Guisheng Wang and Lin Yang and Yizhou Yu
- Abstract summary: We present a new type of discriminator, the segmentor, to accurately locate the lesions and improve the visual quality of pseudo-healthy images.
We apply the generated images into medical image enhancement and utilize the enhanced results to cope with the low contrast problem.
Comprehensive experiments on the T2 modality of BraTS demonstrate that the proposed method substantially outperforms the state-of-the-art methods.
- Score: 68.5287824124996
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Synthesizing a subject-specific pathology-free image from a pathological
image is valuable for algorithm development and clinical practice. In recent
years, several approaches based on the Generative Adversarial Network (GAN)
have achieved promising results in pseudo-healthy synthesis. However, the
discriminator (i.e., a classifier) in the GAN cannot accurately identify
lesions and further hampers from generating admirable pseudo-healthy images. To
address this problem, we present a new type of discriminator, the segmentor, to
accurately locate the lesions and improve the visual quality of pseudo-healthy
images. Then, we apply the generated images into medical image enhancement and
utilize the enhanced results to cope with the low contrast problem existing in
medical image segmentation. Furthermore, a reliable metric is proposed by
utilizing two attributes of label noise to measure the health of synthetic
images. Comprehensive experiments on the T2 modality of BraTS demonstrate that
the proposed method substantially outperforms the state-of-the-art methods. The
method achieves better performance than the existing methods with only 30\% of
the training data. The effectiveness of the proposed method is also
demonstrated on the LiTS and the T1 modality of BraTS. The code and the
pre-trained model of this study are publicly available at
https://github.com/Au3C2/Generator-Versus-Segmentor.
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