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Segmentation of vasculature from fluorescently labeled endothelial cells in multi-photon microscopy images

Bates R, Irving B, Markelc B, Kaeppler J, Brown G, Muschel RJ, Brady SM, Grau V, Schnabel JA (2019)

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Publication Type: Journal article

Publication year: 2019

Journal

Book Volume: 38

Pages Range: 1-10

Article Number: 8006267

Journal Issue: 1

DOI: 10.1109/TMI.2017.2725639

Abstract

Vasculature is known to be of key biological significance, especially in the study of tumors. As such, considerable effort has been focused on the automated segmentation of vasculature in medical and pre-clinical images. The majority of vascular segmentation methods focus on bloodpool labeling methods; however, particularly, in the study of tumors, it is of particular interest to be able to visualize both the perfused and the non-perfused vasculature. Imaging vasculature by highlighting the endothelium provides a way to separate the morphology of vasculature from the potentially confounding factor of perfusion. Here, we present a method for the segmentation of tumor vasculature in 3D fluorescence microscopic images using signals from the endothelial and surrounding cells. We show that our method can provide complete and semantically meaningful segmentations of complex vasculature using a supervoxel-Markov random field approach. We show that in terms of extracting meaningful segmentations of the vasculature, our method outperforms both state-of-the-art method, specific to these data, as well as more classical vasculature segmentation methods.

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How to cite

APA:

Bates, R., Irving, B., Markelc, B., Kaeppler, J., Brown, G., Muschel, R.J.,... Schnabel, J.A. (2019). Segmentation of vasculature from fluorescently labeled endothelial cells in multi-photon microscopy images. IEEE Transactions on Medical Imaging, 38(1), 1-10. https://doi.org/10.1109/TMI.2017.2725639

MLA:

Bates, Russell, et al. "Segmentation of vasculature from fluorescently labeled endothelial cells in multi-photon microscopy images." IEEE Transactions on Medical Imaging 38.1 (2019): 1-10.

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