TY - GEN
T1 - Unsupervised inference of arbor morphology progression for microglia from confocal microscope images
AU - Xu, Yan
AU - Savelonas, Michalis
AU - Qiu, Peng
AU - Trett, Kristen
AU - Shain, William
AU - Roysam, Badrinath
PY - 2013
Y1 - 2013
N2 - Microglia are Central Nervous System (CNS) cells that are similar to macrophages. They undergo characteristic changes in cell arbor morphology in response to tissue perturbation. Ensembles of microglia exhibit a progression of arbor morphologies. Our goal is to discover these progressions and determine the underlying arbor features from 3-D multi-channel fluorescence confocal microscope images of rat brain tissue multiplex stained with Hoechst to reveal cell nuclei, and immunolabeled for IBA-1 to reveal microglia. The microglia are automatically traced, and a set of 131 arbor features are computed. An agglomerative clustering algorithm based on Pearson's correlation is used to derive coherent modules of features. A k-NNG structural similarity analysis of feature modules enables us to construct a global similarity matrix, from which a global multi-level k-NNG is constructed to derive an interactive progression chart through a modified Fruchterman-Reingold algorithm that clearly reveals a progression from highly ramified microglia to round cells proximal to the injury site of an implanted neural recording device.
AB - Microglia are Central Nervous System (CNS) cells that are similar to macrophages. They undergo characteristic changes in cell arbor morphology in response to tissue perturbation. Ensembles of microglia exhibit a progression of arbor morphologies. Our goal is to discover these progressions and determine the underlying arbor features from 3-D multi-channel fluorescence confocal microscope images of rat brain tissue multiplex stained with Hoechst to reveal cell nuclei, and immunolabeled for IBA-1 to reveal microglia. The microglia are automatically traced, and a set of 131 arbor features are computed. An agglomerative clustering algorithm based on Pearson's correlation is used to derive coherent modules of features. A k-NNG structural similarity analysis of feature modules enables us to construct a global similarity matrix, from which a global multi-level k-NNG is constructed to derive an interactive progression chart through a modified Fruchterman-Reingold algorithm that clearly reveals a progression from highly ramified microglia to round cells proximal to the injury site of an implanted neural recording device.
KW - Arbor Analytics
KW - Biomedical Image Analysis
KW - Sample Progression Analysis
UR - http://www.scopus.com/inward/record.url?scp=84881626434&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84881626434&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2013.6556784
DO - 10.1109/ISBI.2013.6556784
M3 - Conference contribution
AN - SCOPUS:84881626434
SN - 9781467364546
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 1356
EP - 1359
BT - ISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging
T2 - 2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013
Y2 - 7 April 2013 through 11 April 2013
ER -