A large multi-focus dataset for white blood cell classification

Abstract

The White Blood Cell (WBC) differential test ranks as the second most frequently performed diagnostic assay. It requires manual confirmation of the peripheral blood smear by experts to identify signs of abnormalities. Automated digital microscopy has emerged as a solution to reduce this labor-intensive process and improve efficiency. Several publicly available datasets provide various WBC subtypes of differing quality and resolution. These datasets have contributed to advancing WBC classification using machine learning techniques. However, digital microscopy of blood cells with high magnification often requires a wider depth of field, posing challenges for automatic digital microscopy that necessitates capturing multiple stacks of focal planes to obtain complete images of specific blood cells. Our dataset provides 25,773 image stacks from 72 patients. The image labels consist of 18 classes encompassing normal and abnormal cells, with two experts reviewing each label. Each image includes 10 z-stacks of cropped 200 by 200 pixel images, captured using a 50X microscope with 400 nm intervals. This study presents a comprehensive multi-focus dataset for WBC classification.

Fig. 1

The process of multi-focus image data acquisition. A stack of 10 images is captured, and a U-net-based segmentation model is used on the best-focus image determined by the Laplacian filter. Then, the bounding box is extracted to find the location of the WBC. Experts examine all z-stacks to confirm the subtype.

Fig. 2

Calculated R² scores between miLab expert classification and manual microscope are shown. Five normal WBC subtypes (neutrophil, lymphocyte, monocyte, eosinophil, basophil) and other immature WBCs are listed. The x-axis corresponds to the percentage of WBC cells of a specific subtype that exist in a ground truth slide. The y-axis represents the percentage of WBCs that miLab detected and classified by our experts. The axes are in logarithmic scale to better visualize data points.