Michael Sieverts (1), Nikita Rabbitt (1), Dilworth Y. Parkinson (2), Douglas W. Sborov (3), Claire Acevedo (1) (4)
IEEE 18th International Conference on e-science (e-science) (11-14 October 2022). DOI: 10.1109/eScience55777.2022.00022

Keywords

Bone, bone marrow, bone matrix, x-ray imaging, synchrotron radiation microtomography, image reconstruction, phase contrast, phase retrieval, semantic segmentation, deep learning, machine learning

Abstract

When studying metabolic disease, it is essential to investigate the disease's effect on multiple tissues and identify any communication, or cross-talk, between organs, tissues, and cells. In bone marrow cancer, adipose tissue triggers inflammation and growth of malignant plasma cells within the bone marrow and results in localized bone loss. Synchrotron radiation microtomography imaging enables 3D quantitative analysis of bone and adipose tissues and provides high resolution to observe local changes in tissue microstructure. However, optimal imaging techniques differ for hard bone tissues (absorption imaging) and soft adipose tissues (phase-contrast imaging). Here we introduce a new technique that leverages image reconstruction and deep learning in combination with the high-resolution imaging capabilities of synchrotron radiation microtomography to gain insight into the marrow microenvironment of human bone samples. This approach allowed for successful tissue segmentation and analysis of human core samples. Using high-resolution images such as these could allow for a better understanding of early bone-related changes that may predict disease progression or bone fractures.

How Our Software Was Used

A U-Net model was trained to perform semantic segmentation of bone and adipose tissue using Dragonfly's Deep Learning Tool. Tissue analysis was performed for both the bone and adipose tissue using a combination of Dragonfly and Python.

Author Affiliation

(1) Department of Mechanical Engineering, University of Utah, Salt Lake City, UT, USA
(2) Advanced Light Source, Lawrence Berkeley Laboratory, Berkeley, CA, USA
(3) Division of Hematology, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
(4) Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA