The topic of this thesis is analysis of digital bone marrow slides using deep learning. Chapter 2 details the development of a convolutional neural network for the automatic segmentation of six different cell/tissue types in bone marrow histopathology images. Using the segmentation output of this neural network, a classifier capable of classifying normocellular and aplastic bone marrow is trained. In Chapter 3, the neural network is applied to a cohort of 130 patients and the segmentation output is used for the automatic quantification of bone marrow cellularity. The age-related decrease of bone marrow cellularity is studied and compared to results in the literature. Also, the agreement between the cellularity quantification and visual estimation by a pathologist is measured. Chapter 4 covers preliminary experiments on the WSI-level classification of four different hematopathologies. Lastly, Chapter 5 summarizes and discusses the results of Chapters 2 through 4.
Deep learning-based analysis of bone marrow histopathology images
L. van Eekelen
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