The strong interaction of blood with the foreign surface of membrane oxygenators during ECMO therapy leads to adhesion of immune cells on the oxygenator membranes, which can be visualized in the form of image sequences using confocal laser scanning microscopy. The segmentation and quantification of these image sequences is a demanding task, but it is essential to understanding the significance of adhering cells during extracorporeal circulation. The aim of this work was to develop and test a deep learning-supported image processing tool (Deetect), suitable for the analysis of confocal image sequences of cell deposits on oxygenator membranes at certain predilection sites. Deetect was tested using confocal image sequences of stained (DAPI) blood cells that adhered to specific predilection sites (junctional warps and hollow fibers) of a phosphorylcholine-coated polymethylpentene membrane oxygenator after patient support (>24 h). Deetect comprises various functions to overcome difficulties that occur during quantification (segmentation, elimination of artifacts). To evaluate Deetects performance, images were counted and segmented manually as a reference and compared with the analysis by a traditional segmentation approach in Fiji and the newly developed tool. Deetect outperformed conventional segmentation in clustered areas. In sections where cell boundaries were difficult to distinguish visually, previously defined post-processing steps of Deetect were applied, resulting in a more objective approach for the resolution of these areas.
Keywords: ECMO; cell adhesion; cell quantification; confocal microscopy; deep learning.