论文标题
双U-NET用于实时单元图像的超分辨率和分割
Double U-Net for Super-Resolution and Segmentation of Live Cell Images
论文作者
论文摘要
实时细胞图像的准确分割在临床和研究环境中具有广泛的应用。深度学习方法已经能够以高精度进行细胞分割。但是，开发机器学习模型需要访问活细胞的高保真图像。由于资源限制（例如对高性能显微镜的可访问性有限或由于研究生物的性质），因此通常无法使用。对活细胞的低分辨率图像进行分割是一项艰巨的任务。本文提出了一种通过将超分辨率作为分割管道中的预处理步骤进行的，以低分辨率图像执行活细胞分割的方法。
Accurate segmentation of live cell images has broad applications in clinical and research contexts. Deep learning methods have been able to perform cell segmentations with high accuracy; however developing machine learning models to do this requires access to high fidelity images of live cells. This is often not available due to resource constraints like limited accessibility to high performance microscopes or due to the nature of the studied organisms. Segmentation on low resolution images of live cells is a difficult task. This paper proposes a method to perform live cell segmentation with low resolution images by performing super-resolution as a pre-processing step in the segmentation pipeline.
