2016/03/05 by Viswanath P. Sudarshan, Sudarshan, Viswanath P, Tobias Weiser +7
Biochemistry, Genetics and Molecular Biology · Medicine · #Cell Image Analysis Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Physical sciences #Image and Video Processing (eess.IV) #Medical Physics (physics.med-ph) #Reproductive Biology and Fertility #Spectroscopy Techniques in Biomedical and Chemical Research #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1603.01739
openalex publication_date 2016/03/05 · openalex created_date 2022/10/04 · openalex updated_date 2026/07/28
Visual observation of Cumulus Oocyte Complexes provides only limited\ninformation about its functional competence, whereas the molecular evaluations\nmethods are cumbersome or costly. Image analysis of mammalian oocytes can\nprovide attractive alternative to address this challenge. However, it is\ncomplex, given the huge number of oocytes under inspection and the subjective\nnature of the features inspected for identification. Supervised machine\nlearning methods like random forest with annotations from expert biologists can\nmake the analysis task standardized and reduces inter-subject variability. We\npresent a semi-automatic framework for predicting the class an oocyte belongs\nto, based on multi-object parametric segmentation on the acquired microscopic\nimage followed by a feature based classification using random forests.\n