Open access deep learning solutions for imaging flow cytometry

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Imaging flow cytometry (IFC), where an image of each individual cell is acquired as it flows through a cytometer can measure hundreds of thousands of individual cells in minutes, combining the high-throughput capabilities of conventional flow cytometry with single-cell imaging. IFC measures not only total fluorescence intensities but also the spatial image of the fluorescence plus bright-field and dark-field images of each cell in a population. The researcher is then able to measure hundreds of different parameters from these images, for example the cell size, shape, granularity and the position and intensity of any fluorescence biomarker in the cell. This rich information captured through IFC makes it an ideal candidate for the use of high-content approaches to specialist multivariate analysis tools which can then cluster similar cells together, identify rare cells in large populations and look for relationships between cells such as stem cell differentiation pathways. Previously we have applied traditional machine learning techniques to the data output from an Imaging Flow Cytometer to perform these types of tasks and developed software solutions to allow a not expert user to apply these methods to their own datasets. While the methods have proven very successful the user must still measure the features using image analysis tools which requires knowledge of image analysis. However recently there has been a revolution in the field of artificial intelligence with the introduction of deep neural networks (DNNs). These DNNs take inspiration from the working of the human brain with many layers all connected to each other with some basic processing rules. Recent advances in the use of mulit-core graphical processing units has provided the computational power required to train these complex networks in a reasonable amount of time and they have found significant improvements over traditional machine learning methods for image recognition problems. One major advantage of these algorithms is that the user is not required to measure cell features or parameters as required for traditional machine learning algorithms. Therefore these DNNs offer the perfect solution to the problem of removing the step of requiring image analysis before the application of machine learning tools. Also the DNN require very large numbers of single cell images for training the networks which make them ideally suited to the analysis of IFC data.In this project we will develop open access software tools to allow a non-expert to input the output file from an IFC directly into a neural network. We will use a variety of existing DNNs which have been optimised for existing image recognition problems (e.g. character recognition) and apply them to a set of IFC datasets. These datasets have been chosen not only to represent a set of specific biological problems but to also pose specific challenges to the machine learning algorithms. We will also develop and train our own DNNs which will be optimised to the specific datasets and make these available. One disadvantage of DNNs is the method the network learns to classify images is hidden from the user and to address this problem we will study the last layers of the network which contain the most specific patterns that the network uses for classifying images. We will correlate these patterns with the cells they identify in order to improve the performance of future network design. Finally we will use these features to visualise the relationships between cells in order to understand the evolution of the cells in a population.