Machine learning classification of complex vasculature structures from in-vivo bone marrow 3D data

Blood vessels inside the bone marrow (BM) play a vital role in the maintenance of hematopoietic stem cell (HSCs). Investigating the interaction of HSCs relative to vasculature has become the main headline for many recent studies. Advances in microscopy and image analysis using mouse models have allo...

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Bibliographic Details
Main Authors: Khorshed, R. A., Celso, C. Lo
Format: Conference Proceeding
Language:English
Subjects:
3D images
Bifurcation
Biomedical imaging
Blood vessels
Bone marrow
Bones
Classification
Decision Tree Classifier
Decision trees
Machine learning
Stem cells
Three dimensional
Three-dimensional displays
Training
Transgenic
vasculature
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Summary:Blood vessels inside the bone marrow (BM) play a vital role in the maintenance of hematopoietic stem cell (HSCs). Investigating the interaction of HSCs relative to vasculature has become the main headline for many recent studies. Advances in microscopy and image analysis using mouse models have allowed detection, identification and automated quantification of HSCs alongside their vascular niche. This resulted in new hypotheses concerning the activation state of HSCs adjacent to different blood vessel types (for example sinusoids vs. arterioles). Identifying the different types of BM vasculature has become critically important, however it still requires the use of complex immunostainings ex vivo or transgenic reporter mouse lines in vivo. To eliminate these requirements and increase the throughput of studies focusing on the HSC niche, we present a machine learning classification approach based on the Decision Tree Classifier to classify different regions of bone marrow vasculature into four distinct classes based on their discriminative features.
ISSN:1945-8452
DOI:10.1109/ISBI.2016.7493485