Evaluating Out-of-the-Box Methods for the Classification of Hematopoietic Cells in Images of Stained Bone Marrow

Abstract

Compared to the analysis of blood cells in microscope images of peripheral blood, bone marrow images are much more challenging for automated cell classification: not only are the cells more densely distributed, there are also significantly more types of hematopoietic cells. So far, several attempts have been made using custom image features and prior knowledge in form of cytoplasm and nuclei segmentations or a restricted number of cell types in peripheral blood. Instead of hand-crafting features and classification methods for bone marrow images, we compare several well-known methods on our more challenging dataset and we show that while generic classical machine learning approaches cannot compete with specialized algorithms, even out-of-the-box deep learning methods already yield valuable results. Our findings indicate that automated analysis of bone marrow images becomes possible with the advent of convolutional neural networks.