20 March 2015 Segmentation of vascular structures and hematopoietic cells in 3D microscopy images and quantitative analysis
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Abstract
In this paper, we present image processing methods for quantitative study of how the bone marrow microenvironment changes (characterized by altered vascular structure and hematopoietic cell distribution) caused by diseases or various factors. We develop algorithms that automatically segment vascular structures and hematopoietic cells in 3-D microscopy images, perform quantitative analysis of the properties of the segmented vascular structures and cells, and examine how such properties change. In processing images, we apply local thresholding to segment vessels, and add post-processing steps to deal with imaging artifacts. We propose an improved watershed algorithm that relies on both intensity and shape information and can separate multiple overlapping cells better than common watershed methods. We then quantitatively compute various features of the vascular structures and hematopoietic cells, such as the branches and sizes of vessels and the distribution of cells. In analyzing vascular properties, we provide algorithms for pruning fake vessel segments and branches based on vessel skeletons. Our algorithms can segment vascular structures and hematopoietic cells with good quality. We use our methods to quantitatively examine the changes in the bone marrow microenvironment caused by the deletion of Notch pathway. Our quantitative analysis reveals property changes in samples with deleted Notch pathway. Our tool is useful for biologists to quantitatively measure changes in the bone marrow microenvironment, for developing possible therapeutic strategies to help the bone marrow microenvironment recovery.
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Jian Mu, Jian Mu, Lin Yang, Lin Yang, Malgorzata M. Kamocka, Malgorzata M. Kamocka, Amy L. Zollman, Amy L. Zollman, Nadia Carlesso, Nadia Carlesso, Danny Z. Chen, Danny Z. Chen, } "Segmentation of vascular structures and hematopoietic cells in 3D microscopy images and quantitative analysis", Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 941305 (20 March 2015); doi: 10.1117/12.2082350; https://doi.org/10.1117/12.2082350
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