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.

翻译：活细胞图像的精确分解在临床和研究环境中具有广泛的应用。深层学习方法能够以高精确度进行细胞分解;然而,为此开发机器学习模型需要获取活细胞的高忠度图像。由于资源限制,例如无法接触高性能显微镜,或由于研究生物的性质,这种情况往往无法实现。对活细胞低分辨率图像进行分解是一项困难的任务。本文提出一种方法,通过将超分辨率作为分解管道的预处理步骤,进行低分辨率图像的活细胞分解。