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Open Access Green as soon as Postprint is submitted to ZB.
Segmentation of vasculature from fluorescently labeled endothelial cells in multi-photon microscopy images.
IEEE Trans. Med. Imaging 38, 1-10 (2019)
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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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Image Segmentation ; Machine Learning ; Markov Random Fields ; Microscopy
ISSN (print) / ISBN
0278-0062
e-ISSN
1558-254X
Quellenangaben
Volume: 38,
Issue: 1,
Pages: 1-10
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Publishing Place
New York, NY [u.a.]
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute for Machine Learning in Biomed Imaging (IML)