TY - GEN
T1 - Detecting abnormal cell division patterns in early stage human embryo development
AU - Khan, Aisha
AU - Gould, Stephen
AU - Salzmann, Mathieu
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - Recently, it has been shown that early division patterns, such as cell division timing biomarkers, are crucial to predict human embryo viability. Precise and accurate measurement of these markers requires cell lineage analysis to identify normal and abnormal division patterns. However, current approaches to early-stage embryo analysis only focus on estimating the number of cells and their locations, thus failing to detect abnormal division patterns and potentially yielding incorrect timing biomarkers. In this work we propose an automated tool that can perform lineage tree analysis up to the 5-cell stage, which is sufficient to accurately compute all the known important biomarkers. To this end, we introduce a CRF-based cell localization framework. We demonstrate the benefits of our approach on a data set of 22 human embryos, resulting in correct identification of all abnormal division patterns in the data set.
AB - Recently, it has been shown that early division patterns, such as cell division timing biomarkers, are crucial to predict human embryo viability. Precise and accurate measurement of these markers requires cell lineage analysis to identify normal and abnormal division patterns. However, current approaches to early-stage embryo analysis only focus on estimating the number of cells and their locations, thus failing to detect abnormal division patterns and potentially yielding incorrect timing biomarkers. In this work we propose an automated tool that can perform lineage tree analysis up to the 5-cell stage, which is sufficient to accurately compute all the known important biomarkers. To this end, we introduce a CRF-based cell localization framework. We demonstrate the benefits of our approach on a data set of 22 human embryos, resulting in correct identification of all abnormal division patterns in the data set.
UR - http://www.scopus.com/inward/record.url?scp=84952028489&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-24888-2_20
DO - 10.1007/978-3-319-24888-2_20
M3 - Conference contribution
SN - 9783319248875
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 161
EP - 169
BT - Machine Learning in Medical Imaging - 6th International Workshop, MLMI 2015 Held in Conjunction with MICCAI 2015, Proceedings
A2 - Zhou, Luping
A2 - Shi, Yinghuan
A2 - Wang, Li
A2 - Wang, Qian
PB - Springer Verlag
T2 - 6th International Workshop on Machine Learning in Medical Imaging, MLMI 2015 and Held in Conjunction with 18th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2015
Y2 - 5 October 2015 through 5 October 2015
ER -