Generative multi-adversarial network for striking the right balance in abdominal image segmentation.
Generative multi-adversarial network for striking the right balance in abdominal image segmentation.
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DOI：
10.1007/s11548-020-02254-4
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发表时间：
2020-11
影响因子：
3
通讯作者：
Yoshida H
中科院分区：
文献类型：
--
作者：
Rezaei M;Näppi JJ;Lippert C;Meinel C;Yoshida H
目的识别正常解剖结构中相对罕见的异常是医学图像语义分割中深度学习的主要挑战。训练数据中少数类的样本数量较少，这给最优分类的学习带来了挑战，而大多数类中出现频率较高的样本阻碍了罕见目标对象与类之间分类边界的泛化。本文针对腹部图像语义分割中的类别失衡问题，提出了一种新的产生式多对偶网络--Ensymble-GAN。方法集成-GAN框架由单生成器和多鉴别器变量组成，用于处理类不平衡问题，提供了比现有方法更好的泛化能力。集成模型通过来自不同初始化的训练和来自训练数据的各个子集的损失来聚集多个模型的估计。单生成器网络分析输入图像作为条件，以通过使用来自鉴别器网络集成的反馈来预测相应的语义分割图像。为了评估该框架，我们在两个公共数据集上训练了我们的框架，它们具有不同的不平衡比率和成像模式：混沌2019和LITS 2017。结果根据F1评分，正常脾、肝、左、右肾的语义分割准确率分别为0.93、0.96、0.90、0.94。同时分割病变和肝脏的总体F1评分分别为0.83和0.94。结论在医学图像的语义分割方面，与其他常用的腹部成像基准方法相比，该框架在医学图像的语义分割方面表现出了优异的性能。与人类专家相比，EnSemble-GAN具有更准确地分割腹部图像的潜力。
Purpose The identification of abnormalities that are relatively rare within otherwise normal anatomy is a major challenge for deep learning in the semantic segmentation of medical images. The small number of samples of the minority classes in the training data makes the learning of optimal classification challenging, while the more frequently occurring samples of the majority class hamper the generalization of the classification boundary between infrequently occurring target objects and classes. In this paper, we developed a novel generative multi-adversarial network, called Ensemble-GAN, for mitigating this class imbalance problem in the semantic segmentation of abdominal images. Method The Ensemble-GAN framework is composed of a single-generator and a multi-discriminator variant for handling the class imbalance problem to provide a better generalization than existing approaches. The ensemble model aggregates the estimates of multiple models by training from different initializations and losses from various subsets of the training data. The single generator network analyzes the input image as a condition to predict a corresponding semantic segmentation image by use of feedback from the ensemble of discriminator networks. To evaluate the framework, we trained our framework on two public datasets, with different imbalance ratios and imaging modalities: the Chaos 2019 and the LiTS 2017. Result In terms of the F1 score, the accuracies of the semantic segmentation of healthy spleen, liver, and left and right kidneys were 0.93, 0.96, 0.90 and 0.94, respectively. The overall F1 scores for simultaneous segmentation of the lesions and liver were 0.83 and 0.94, respectively. Conclusion The proposed Ensemble-GAN framework demonstrated outstanding performance in the semantic segmentation of medical images in comparison with other approaches on popular abdominal imaging benchmarks. The Ensemble-GAN has the potential to segment abdominal images more accurately than human experts.
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影响因子：
10.6
作者：
Warfield, SK;Zou, KH;Wells, WM
通讯作者：
Wells, WM
DOI：
10.1007/978-3-319-67558-9_28
发表时间：
2017-09-09
期刊：
Deep learning in medical image analysis and multimodal learning for clinical decision support : Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, held in conjunction with MICCAI 2017 Quebec City, QC,..
影响因子：
--
作者：
Sudre CH;Li W;Vercauteren T;Ourselin S;Jorge Cardoso M
通讯作者：
Jorge Cardoso M
影响因子：
8
作者：
Sun, Yamnin;Kamel, Mohamed S.;Wang, Yang
通讯作者：
Wang, Yang
DOI：
10.24818/18423264/52.1.18.13
发表时间：
2018-01-01
影响因子：
0.9
作者：
Moon, Kyoung-Sook;Jun, Sookyung;Kim, Hongjoong
通讯作者：
Kim, Hongjoong
影响因子：
3.8
作者：
Han, Xiao
通讯作者：
Han, Xiao