Geometry-Aware Cell Detection with Deep Learning

Automated diagnostic microscopy powered by deep learning is useful, particularly in rural areas. However, there is no general method for object detection of different cells. In this study, we developed GFS-ExtremeNet, a geometry-aware deep-learning method which is based on the detection of four extreme key points for each object (topmost, bottommost, rightmost, and leftmost) and its center point. A postprocessing step, namely, adjacency spectrum, was employed to measure whether the distances between the key points were below a certain threshold for a particular cell candidate. Our newly proposed geometry-aware deep-learning method outperformed other conventional object detection methods and could be applied to any type of cell with a certain geometrical order. Our GFS-ExtremeNet approach opens a new window for the development of an automated cell detection system. ABSTRACT Analyzing cells and tissues under a microscope is a cornerstone of biological research and clinical practice. However, the challenge faced by conventional microscopy image analysis is the fact that cell recognition through a microscope is still time-consuming and lacks both accuracy and consistency. Despite enormous progress in computer-aided microscopy cell detection, especially with recent deep-learning-based techniques, it is still difficult to translate an established method directly to a new cell target without extensive modification. The morphology of a cell is complex and highly varied, but it has long been known that cells show a nonrandom geometrical order in which a distinct and defined shape can be formed in a given type of cell. Thus, we have proposed a geometry-aware deep-learning method, geometric-feature spectrum ExtremeNet (GFS-ExtremeNet), for cell detection. GFS-ExtremeNet is built on the framework of ExtremeNet with a collection of geometric features, resulting in the accurate detection of any given cell target. We obtained promising detection results with microscopic images of publicly available mammalian cell nuclei and newly collected protozoa, whose cell shapes and sizes varied. Even more striking, our method was able to detect unicellular parasites within red blood cells without misdiagnosis of each other. IMPORTANCE Automated diagnostic microscopy powered by deep learning is useful, particularly in rural areas. However, there is no general method for object detection of different cells. In this study, we developed GFS-ExtremeNet, a geometry-aware deep-learning method which is based on the detection of four extreme key points for each object (topmost, bottommost, rightmost, and leftmost) and its center point. A postprocessing step, namely, adjacency spectrum, was employed to measure whether the distances between the key points were below a certain threshold for a particular cell candidate. Our newly proposed geometry-aware deep-learning method outperformed other conventional object detection methods and could be applied to any type of cell with a certain geometrical order. Our GFS-ExtremeNet approach opens a new window for the development of an automated cell detection system.