TY - GEN
T1 - Automated localization and quantification of protein multiplexes via multispectral fluorescence imaging
AU - Teverovskiy, Mikhail
AU - Vengrenyuk, Yevgen
AU - Tabesh, Ali
AU - Sapir, Marina
AU - Fogarasi, Stephen
AU - Pang, Ho Yuen
AU - Khan, Faisal M.
AU - Hamann, Stefan
AU - Capodieci, Paola
AU - Clayton, Mark
AU - Kim, Robert
AU - Fernandez, Gerardo
AU - Mesa-Tejada, Ricardo
AU - Donovan, Michael J.
N1 - Funding Information:
W. J. Ong acknowledges financial assistance and faculty start-up grants/supports from Xiamen University. This work is supported by Xiamen University Malaysia Research Fund (XMUMRF/2019-C3/IENG/0013). W. J. Ong would also like to thank Petronas, ExxonMobil and Shell Malaysia for granting him the ‘ 2018 Merdeka Award Grant ’.
PY - 2008
Y1 - 2008
N2 - We present a new system for automated localization and quantification of the expression of protein biomarkers in immunofluorescence (IF) microscopic images. The system includes a novel method for discriminating the biomarker signal from background, where signal may be the expression of any of the many biomarkers or counterstains used in IF. The method is based on supervised learning and represents the biomarker intensity threshold as a function of image background characteristics. The utility of the proposed system is demonstrated in predicting prostate cancer recurrence in patients undergoing prostatectomy. Specifically, features representing androgen receptor (AR) expression are shown to be statistically significantly associated with poor outcome in univariate analysis. AR features are also shown to be valuable for multivariate recurrence prediction.
AB - We present a new system for automated localization and quantification of the expression of protein biomarkers in immunofluorescence (IF) microscopic images. The system includes a novel method for discriminating the biomarker signal from background, where signal may be the expression of any of the many biomarkers or counterstains used in IF. The method is based on supervised learning and represents the biomarker intensity threshold as a function of image background characteristics. The utility of the proposed system is demonstrated in predicting prostate cancer recurrence in patients undergoing prostatectomy. Specifically, features representing androgen receptor (AR) expression are shown to be statistically significantly associated with poor outcome in univariate analysis. AR features are also shown to be valuable for multivariate recurrence prediction.
KW - Fluorescence microscopy
KW - Image thresholding
KW - Immunofluorescence
KW - Multispectral imaging
KW - Prostate cancer
UR - http://www.scopus.com/inward/record.url?scp=51049106175&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2008.4540992
DO - 10.1109/ISBI.2008.4540992
M3 - Conference contribution
AN - SCOPUS:51049106175
SN - 9781424420032
T3 - 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI
SP - 300
EP - 303
BT - 2008 5th IEEE International Symposium on Biomedical Imaging
T2 - 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI
Y2 - 14 May 2008 through 17 May 2008
ER -