Classification of blood cells

A system comprising:

• a microfluidic device configured to contain a blood sample, the microfluidic device comprising a channel configured to emulate blood vessels in vivo, wherein the channel comprises at least one functionalized adhesion region adapted to adhere to blood cells of interest within the blood sample;
• a processor;
• and
• one or more non-transitory machine readable media to store instructions and data, the data including an input image of the blood sample within the channel, the processor configured to access the media and execute the instructions comprising:
• a first machine learning model trained to detect red blood cells and/or subtypes of red blood cells and generate a segmentation mask that assigns pixels in the input image to one of a plurality of classes that correlate to respective known biophysical properties of red blood cells;
• extraction code programmed to extract cell images from the input image based on the segmentation mask, in which each extracted cell image includes a respective cluster of the pixels assigned to a respective one of the plurality of classes;
• and
• a second machine learning model trained to classify the extracted cell images to specify morphological subtypes for the red blood cells and/or the subtypes of red blood cells detected by the first machine learning model.

A system comprising:

• a microfluidic device configured to contain a blood sample, the microfluidic device comprising a channel configured to emulate blood vessels in vivo;
• a processor;
• and
• one or more non-transitory machine readable media to store instructions and data, the data including an input image of the blood sample within the channel, the processor configured to access the media and execute the instructions comprising:
• a first machine learning model trained to detect red blood cells and/or subtypes of red blood cells and generate a segmentation mask that assigns pixels in the input image to one of a plurality of classes that correlate to respective known biophysical properties of red blood cells;
• extraction code programmed to extract cell images from the input image based on the segmentation mask, in which each extracted cell image includes a respective cluster of the pixels assigned to a respective one of the plurality of classes;
• and
• a second machine learning model trained to classify the extracted cell images to specify morphological subtypes for the red blood cells and/or the subtypes of red blood cells detected by the first machine learning model,
• wherein the extraction code is further programmed to:
• cluster the pixels in the segmentation mask to define respective pixel clusters according to the assigned class to detect cell objects in the input image;
• identify centroids for each respective pixel cluster;
• and
• detect bounding boxes around each of the identified centroids, wherein each extracted cell image is generated based on pixels within the detected bounding box.