Algorithm for the precise detection of single and cluster cells in microfluidic applications

Abstract

Recent advances in imaging flow cytometry and microfluidic applications have led to the development of suitable mathematical algorithms capable of detecting and identifying targeted cells in images. In contrast to currently existing algorithms, we herein proposed the identification and reconstruction of cell edges based on original approaches that overcome frequent detection limitations such as halos, noise, and droplet boundaries in microfluidic applications. Reconstructed cells are then discriminated between single cells and clusters of round-shaped cells, and cell information such as the area and location of a cell in an image is output. Using this method, 76% of cells detected in an image had an error <5% of the cell area size and 41% of the image had an error <1% of the cell area size (n 5 1,000). The method developed in the present study is the first image processing algorithm designed to be flexible in use (i.e. independent of the size of an image, using a microfluidic droplet system or not, and able to recognize cell clusters in an image) and provides the scientific community with a very accurate imaging algorithm in the field of microfluidic applications. V C 2016 International Society for Advancement of CytometryKey terms algorithm; microfluidic; cell detection; cell reconstruction; imaging processing; droplet THE morphological identification of a cell offers numerous advantages when discriminating elements such as cell populations based on their shapes. However, this time-consuming method is limited to the skills of the operator and is associated with difficulty in imaging cell sorters where quick decisions must be made based on the information contained in each image. The combination of a high speed camera and algorithm has been proposed in order to routinely and rapidly measure the concentration of target cells in a sample. This recent imaging cytometric approach associated with microfluidic devices indicates that the automatic detection of a cell in an image is applicable to the identification of rare cell types (e.g., possible circulating tumor cells) without any fluorescence label (1). In this context, the morphological properties of a cell such as size area, diameter, and circularity must be calculated with the highest precision in order to limit inaccurate diagnoses.In the image processing field, numerous algorithms have been created and improved with the aim of correctly identifying the edge of an object and providing reliable data in greyscale and color images (2-7). However, in imaging flow cytometric systems and microfluidic devices, cells moving in the flow may hamper these common edge detectors and led to the incomplete detection of cell walls. To overcome this limitation and improve the detection of a cell in an image, several specific algorithms have been developed in the past decade (8-11). Although these algorithms provided interesting findings, some common limitations remained. In point of view of the flexibility in use, development of specific algorithms leads to focus on ca...

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