Related papers: Generator Versus Segmentor: Pseudo-healthy Synthesis

Generator Versus Segmentor: Pseudo-healthy Synthesis

URL: http://arxiv.org/abs/2009.05722v3
Date: Thu, 15 Jul 2021 13:59:39 GMT
Title: Generator Versus Segmentor: Pseudo-healthy Synthesis
Authors: Zhang Yunlong, Li Chenxin, Lin Xin, Sun Liyan, Zhuang Yihong, Huang Yue, Ding Xinghao, Liu Xiaoqing, Yu Yizhou
Abstract summary: We propose a novel adversarial training regime, Generator versus Segmentor (GVS), to alleviate this trade-off. We also propose a new metric to measure how healthy the synthetic images look.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: This paper investigates the problem of pseudo-healthy synthesis that is defined as synthesizing a subject-specific pathology-free image from a pathological one. Recent approaches based on Generative Adversarial Network (GAN) have been developed for this task. However, these methods will inevitably fall into the trade-off between preserving the subject-specific identity and generating healthy-like appearances. To overcome this challenge, we propose a novel adversarial training regime, Generator versus Segmentor (GVS), to alleviate this trade-off by a divide-and-conquer strategy. We further consider the deteriorating generalization performance of the segmentor throughout the training and develop a pixel-wise weighted loss by muting the well-transformed pixels to promote it. Moreover, we propose a new metric to measure how healthy the synthetic images look. The qualitative and quantitative experiments on the public dataset BraTS demonstrate that the proposed method outperforms the existing methods. Besides, we also certify the effectiveness of our method on datasets LiTS. Our implementation and pre-trained networks are publicly available at https://github.com/Au3C2/Generator-Versus-Segmentor.

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