Grading of Mammalian Cumulus Oocyte Complexes using Machine Learning for in Vitro Embryo Culture

Autor:	Sudarshan, Viswanath P, Weiser, Tobias, Chintala, Phalgun, Mandal, Subhamoy, Dutta, Rahul
Rok vydání:	2016
Předmět:	Computer Science - Computer Vision and Pattern Recognition Electrical Engineering and Systems Science - Image and Video Processing Physics - Medical Physics
Druh dokumentu:	Working Paper
Popis:	Visual observation of Cumulus Oocyte Complexes provides only limited information about its functional competence, whereas the molecular evaluations methods are cumbersome or costly. Image analysis of mammalian oocytes can provide attractive alternative to address this challenge. However, it is complex, given the huge number of oocytes under inspection and the subjective nature of the features inspected for identification. Supervised machine learning methods like random forest with annotations from expert biologists can make the analysis task standardized and reduces inter-subject variability. We present a semi-automatic framework for predicting the class an oocyte belongs to, based on multi-object parametric segmentation on the acquired microscopic image followed by a feature based classification using random forests. Comment: IEEE BHI 2016
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1603.01739 Zobrazit plný text záznamu View this record from Arxiv