Special Section on Image Cytometry

COMPUTERIZED image cytometry is more than 60 years old. The seminal paper from 1966 by Judith Prewitt starts with the statement “Automation of the acquisition and interpretation of data in microscopy has been a focus of biomedical research for almost a decade” (Prewitt) (1). The available computer technology has developed tremendously since those days with many million times more computer capacity per dollar available to us. The image analysis methods have also improved significantly with better segmentation and quantification methods. But until recently, all the methods have been designed by human computer programming of algorithms, where each step has been crafted to detect some feature of the cell such as edges, color contrast, shape, and so forth. With the introduction of artificial intelligence and machine learning to cytometry in the form of deep learning neural networks, that is currently under dramatic change. In a rapidly increasing number of applications, we are finding that computers through machine learning can figure out how to solve image analysis problems significantly better then we can do by writing programs. The concept of using neural network types of computational architectures for image analysis is old (Rosenblatt) (2), but led to very little success until a few years ago. It turned out that significant computational power and thousands of images that could be used as training data were needed to train the networks to really work. During the last decade, the field has exploded and delivered impressive results for a large variety of tasks (Romo-Bucheli) (3). But, it can be hard to understand what is happening in this rapidly evolving field. The manuscript “Deep Learning in Image Cytometry: A Review” in this special section provides a tutorial style introduction and a well-structured overview of the several hundred papers describing cytometry applications that have already been published (this issue, pp. 366–380). Anyone interested in analyzing image cytometry data should go through the review be able to find a paper describing experiences from applying neural networks to a similar task and thus get a better understanding of what it would involve to start using this new technology. When cells or tissue samples are to be studied in a microscope, they are usually stained to enhance structures or components of interest. The most common stain is H&E which basically shows the overall morphological structure of the tissue. Much more information can be obtained through specific stains that bind to a unique protein of interest. Immunofluorescent stains can reveal the presence of some key protein, for example, Ki67 in breast cancer. Through the use of multiple stains with different colors that binds to different proteins more information about the situation in the tissue under study can be obtained. But the number of colors, that is, spectral channels that can be utilized is limited, too many stain colors leads to spectral overlap and cross talk that distorts the information of interest. To overcome this problem, many clever solutions have been developed over the last couple of decades. An early approach was to stain and destain the same tissue section several times. After each staining, it was scanned to create a digital image. These images were registered to each other through image processing (Wählby) (4). Another early approach was to photo-bleach the specimen between multiple scans followed by restaining either to analyze cells systems (Mittag) (5) or protein locations within single cells (Toponomics) (Schubert) (6), (Schubert) (7). The cell system analysis technique was later commercialized (Hennig) (8) and combined with a