Automatic Image Segmentation of Monocytes and Index Computation Using Deep Learning

The classification of white cells plays an important part in medical diagnosis. The counts may suggest the presence of infection, inflammation, anemia, bleeding, and other blood-associated issues. More specifically, the counting in our study is the calculation of the Monocyte Index (MI). The purpose of MI is to determine whether the patient can receive units of blood by analyzing the assay. In case of incompatible blood transfusion, monocytes may ingest or adhere red cells. The index is the percentage of red cells adhered, ingested, or both, versus free monocytes. Manual methods for blood cell counting may take several hours and are highly prone to different sources of errors. Automatic methods, such as Linear Discriminant Analysis, Quadratic Discriminant Analysis, K-Nearest Neighbors, Naïve Bayes, Support Vector Machine, Convolutional Neural Network (CNN), Fast Region-based CNN, Faster Region-based CNN, Spatial Pyramidal Pooling network, Single Shot Detector and Mask Region-based CNN, exist for classification. However, these methods currently do not perform automatic counting and calculation of MI. The dataset is our own collection of images using ZEISS Axiocam 208 color/202 mono microscope camera. For the labels in our own collection, we performed polygonal annotation using the VGG Annotator tool. We trained the Mask R-CNN deep neural network model for automatic segmentation at the pixel-level, using COCO pre-trained weights. Our results look promising, as the Mask R-CNN can perform automatic segmentation with 72% accuracy. Compared to a medical laboratory scientist, the model can process large amount of data simultaneously, quickly and efficiently, with approximately the same judgment accuracy as a human eye. This may significantly reduce the burden of the laboratory scientist and provide a useful reference for doctors to identify a potential blood candidate to be transfused.