Cross Modality Microscopy Segmentation via Adversarial Adaptation.
Cross Modality Microscopy Segmentation via Adversarial Adaptation.
复制标题
通过对抗性适应进行跨模态显微镜分割。
DOI：
10.1007/978-3-030-17935-9_42
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Krishnamurthy,Ashok
中科院分区：
文献类型：
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作者：
Guo,Yue;Wang,Qian;Krupa,Oleh;Stein,Jason;Wu,Guorong;Bradford,Kira;Krishnamurthy,Ashok
深度学习技术已成功应用于自动分割和量化从共焦和光片荧光显微镜获取的图像中的细胞类型。然而，深度学习网络的训练需要大量手动标记的训练数据，这是一个非常耗时的操作。在本文中，我们演示了一种对抗性适应方法，可将显微镜分割的深层网络知识从一种成像方式（例如共焦）转移到一种新的成像方式（例如光片），而这种方式没有或非常有限的标记训练数据可用。有前景的分割结果表明，所提出的迁移学习方法是快速开发新成像方法分割解决方案的有效方法。
Deep learning techniques have been successfully applied to automatically segment and quantify cell-types in images acquired from both confocal and light sheet fluorescence microscopy. However, the training of deep learning networks requires a massive amount of manually-labeled training data, which is a very time-consuming operation. In this paper, we demonstrate an adversarial adaptation method to transfer deep network knowledge for microscopy segmentation from one imaging modality (e.g., confocal) to a new imaging modality (e.g., light sheet) for which no or very limited labeled training data is available. Promising segmentation results show that the proposed transfer learning approach is an effective way to rapidly develop segmentation solutions for new imaging methods.