Generative multi-adversarial network for striking the right balance in abdominal image segmentation
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ORIGINAL ARTICLE
Generative multi-adversarial network for striking the right balance in abdominal image segmentation Mina Rezaei1
· Janne J. Näppi2 · Christoph Lippert1 · Christoph Meinel1 · Hiroyuki Yoshida2
Received: 16 January 2020 / Accepted: 21 August 2020 © The Author(s) 2020
Abstract 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. Keywords Imbalanced learning · Generative multi-discriminative networks · Semantic segmentation · Abdominal imaging
Introduction
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Mina Rezaei [email protected], [email protected] Janne J. Näppi [email protected] Christoph Lippert [email protected] Christoph Meinel [email protected] Hiroyuki Yoshida [email protected]
1
Hasso Plattner Institute, Prof.Dr. Helmert Street 2-3, Potsdam, Germany
2
Massachusetts General Hospital and Harvard Medical School, 25 New Chardon St., Boston, MS, USA
One of the major challenges of deep learning for medical image analysis is the highly skewed class distribution of objects in medical images, which is referred to as the imbalanced classification problem. An imbalanced classification
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