U-Net: deep learning for cell counting, detection, and morphometry
U-Net: deep learning for cell counting, detection, and morphometry
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DOI：
10.1038/s41592-018-0261-2
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发表时间：
2019-01-01
期刊：
影响因子：
48
通讯作者：
Ronneberger, Olaf
中科院分区：
文献类型：
--
作者：
Falk, Thorsten;Mai, Dominic;Ronneberger, Olaf
U-Net是一种通用的深度学习解决方案，用于频繁发生的量化任务，例如生物医学图像数据中的细胞检测和形状测量。我们提出了一个ImageJ插件，使非机器学习专家能够在本地计算机或远程服务器/云服务上使用U-Net分析他们的数据。该插件带有用于单细胞分割的预训练模型，并允许U-Net基于一些注释样本适应新任务。
U-Net is a generic deep-learning solution for frequently occurring quantification tasks such as cell detection and shape measurements in biomedical image data. We present an ImageJ plugin that enables non-machine-learning experts to analyze their data with U-Net on either a local computer or a remote server/cloud service. The plugin comes with pretrained models for single-cell segmentation and allows for U-Net to be adapted to new tasks on the basis of a few annotated samples.