Meeting Banner
Abstract #3515

Automatic segmentation of fetal brain components from MRI using deep learning

Ori Ben Zvi1,2, Netanell Avisdris1,3, Bossmat Yehuda1,2, Daphna Link Sourani1, Leo Joskowicz3, Elka Miller4, Liat Ben Sira2,5, and Dafna Ben Bashat1,2,5
1Sagol Brain Institute, Tel Aviv Sourasky Medical Center; Israel, Tel Aviv, Israel, 2Sagol School of Neuroscience, Tel Aviv University; Israel, Tel Aviv, Israel, 3School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel, Jerusalem, Israel, 4Medical Imaging, Children’s Hospital of Eastern Ontario, University of Ottawa, Ottawa, Canada, Ottawa, ON, Canada, 5Sackler Faculty of Medicine, Tel Aviv University; Israel, Tel Aviv, Israel

Segmentation of the fetal brain into its components is important for quantitative assessment of fetal development. This study proposes a fully automatic method based on deep learning for fetal brain segmentation into six components, including a separation of right and left hemispheres. The method’s performance demonstrated high Dice scores for all brain components and robustness to different contrasts, scan resolutions, gestational age and fetal brain pathologies. Preliminary results demonstrated significant larger ventricle’s volumes and asymmetry in fetuses with ventriculomegaly compared to normal fetuses. The method is suggested to improve fetal assessment and assist radiologists in routine clinical practice.

This abstract and the presentation materials are available to members only; a login is required.

Join Here