Just before the final softmax multi-class output, we placed a dense layer of 128 units to extract 128 features resulting from the training. For the so-called keras features, we used a simple convolutional neural network (CNN) consisting of several stacks of convolutional and drop layers. Feature-value extraction was highly facilitated by our publicly available IFC package, allowing us to batch extract feature values directly from daf files created by the IDEAS® software.
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IDEAS® analysis software was used to process the acquired data to generate the so-called ideas features from brightfield images.
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Added value of this studyĬomputations were performed using a 2 x Xeon E5-2620 (total 12 physical cores) analysis station, bought with the IS but upgraded with a NVIDIA GTX1050Ti (4 GB) and extra RAM (total 48 GB), and windows 7 with R and the abind, caret, e1071, keras, IFC, and tensorflow R packages (software versions are provided in Suppl. As a matter of facts, they confirmed the conclusions of the International Council for Standardisation in Haematology for the need of new tools to allow reliable, high-throughput, and automated quantification of red blood cells. PubMed search provided preliminary resources and even some methods for the automatic assessment of blood cells morphologies but far from a potential application in routine. Red blood cell diseases seem to be particularly suitable for this type of approach, especially those that require urgent diagnosis but suffer from a lack of standardisation such as schistocyte identification and count. Imaging flow cytometry combined with machine learning appears to be a promising tool in haematology, allowing a high input blood cell recognition and quantification.
We searched PubMed using the search terms “imaging flow cytometry”, “learning”, “red blood cell morphology”, “schistocyte” and associated permutations to find relevant articles until March 2018.
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