When: Oct 12th, 2022 – 11:00 – 11:45 AM
Where: Google meet link
A Deep Learning approach for oocytes segmentation and analysis
Medical Assisted Procreation (MAP) has seen a sharp increase in demand over the past decade, due to a variety of reasons, including genetic factors, health conditions altered by stress and pollution, as well as delayed pregnancy and age-related loss of fertility. The success of MAP techniques is strongly correlated to the dexterity of a human operator, who is asked to classify and select healthy oocytes to fertilize and return to the uterus. This work describes a deep learning approach to the segmentation of oocyte images, to support operators in their selection, to improve the success probability of MAP.