Cell instance segmentation is a fundamental task for many biological applications,especially for packed cells in three-dimensional(3D)microscope images that can fully display cellular morphology.Image processing algor...Cell instance segmentation is a fundamental task for many biological applications,especially for packed cells in three-dimensional(3D)microscope images that can fully display cellular morphology.Image processing algorithms based on neural networks and feature engineering have enabled great progress in two-dimensional(2D)instance segmentation.However,current methods cannot achieve high segmentation accuracy for irregular cells in 3D images.In this study,we introduce a universal,morphology-based 3D instance segmentation algorithm called Crop Once Merge Twice(C1M2),which can segment cells from a wide range of image types and does not require nucleus images.C1M2 can be extended to quantify the fluorescence intensity of fluorescent proteins and antibodies and automatically annotate their expression levels in individual cells.Our results suggest that C1M2 can serve as a tissue cytometry for 3D histopathological assays by quantifying fluorescence intensity with spatial localization and morphological information.展开更多
基金the National Key Research and Development Program of China(2017YFA0700403,2017YFA0700402)the National Natural Science Foundation of China(62061160490)+2 种基金the Applied Fundamental Research of Wuhan(2020010601012167)the Fundamental Research Funds for the Central Universities(2019kfy XMBZ022)the Innovation Fund of Wuhan National Laboratory for Optoelectronics(WNLO)。
文摘Cell instance segmentation is a fundamental task for many biological applications,especially for packed cells in three-dimensional(3D)microscope images that can fully display cellular morphology.Image processing algorithms based on neural networks and feature engineering have enabled great progress in two-dimensional(2D)instance segmentation.However,current methods cannot achieve high segmentation accuracy for irregular cells in 3D images.In this study,we introduce a universal,morphology-based 3D instance segmentation algorithm called Crop Once Merge Twice(C1M2),which can segment cells from a wide range of image types and does not require nucleus images.C1M2 can be extended to quantify the fluorescence intensity of fluorescent proteins and antibodies and automatically annotate their expression levels in individual cells.Our results suggest that C1M2 can serve as a tissue cytometry for 3D histopathological assays by quantifying fluorescence intensity with spatial localization and morphological information.
