Automated Measurements of Key Morphological Features of Human Embryos for IVF.
Med Image Comput Comput Assist Interv
; 12265: 25-35, 2020 Oct.
Article
in En
| MEDLINE
| ID: mdl-33313603
ABSTRACT
A major challenge in clinical In-Vitro Fertilization (IVF) is selecting the highest quality embryo to transfer to the patient in the hopes of achieving a pregnancy. Time-lapse microscopy provides clinicians with a wealth of information for selecting embryos. However, the resulting movies of embryos are currently analyzed manually, which is time consuming and subjective. Here, we automate feature extraction of time-lapse microscopy of human embryos with a machine-learning pipeline of five convolutional neural networks (CNNs). Our pipeline consists of (1) semantic segmentation of the regions of the embryo, (2) regression predictions of fragment severity, (3) classification of the developmental stage, and object instance segmentation of (4) cells and (5) pronuclei. Our approach greatly speeds up the measurement of quantitative, biologically relevant features that may aid in embryo selection.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Med Image Comput Comput Assist Interv
Journal subject:
DIAGNOSTICO POR IMAGEM
/
INFORMATICA MEDICA
Year:
2020
Document type:
Article
Affiliation country:
United States