@inproceedings{648311a4331949328282dc9193b45916,
title = "3D segmentation of glial cells using fully convolutional networks and k-terminal cut",
abstract = "Glial cells play an important role in regulating synaptogenesis,development of blood-brain barrier,and brain tumor metastasis. Quantitative analysis of glial cells can offer new insights to many studies. However,the complicated morphology of the protrusions of glial cells and the entangled cell-to-cell network cause significant difficulties to extracting quantitative information in images. In this paper,we present a new method for instance-level segmentation of glial cells in 3D images. First,we obtain accurate voxel-level segmentation by leveraging the recent advances of fully convolutional networks (FCN). Then we develop a kterminal cut algorithm to disentangle the complex cell-to-cell connections. During the cell cutting process,to better capture the nature of glial cells,a shape prior computed based on a multiplicative Voronoi diagram is exploited. Extensive experiments using real 3D images show that our method has superior performance over the state-of-the-art methods.",
author = "Lin Yang and Yizhe Zhang and Guldner, {Ian H.} and Siyuan Zhang and Chen, {Danny Z.}",
note = "Funding Information: This research was supported in part by NSF Grants CCF-1217906 and CCF-1617735, and by NIH Grant 5R01CA194697-02. Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.",
year = "2016",
doi = "10.1007/978-3-319-46723-8_76",
language = "English (US)",
isbn = "9783319467221",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "658--666",
editor = "Gozde Unal and Sebastian Ourselin and Leo Joskowicz and Sabuncu, {Mert R.} and William Wells",
booktitle = "Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings",
}