U-Net: deep learning for cell counting, detection, and morphometry
Nature methods, 2019•nature.com
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.
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.
Abstract
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.
nature.com
