Fetal Brain Segmentation Using Deep Learning
Deep learning is no longer groundbreaking in the field of medical imaging. As outstanding developers have publicly released their source codes in public, it has been widely exploited not only for segmentation of tumor and brain injuries but also for clinical diagnostic services assisted by artificial intelligence. Pediatric radiologists have also kept their eyes on the applicability of deep learning technologies. In in utero magnetic resonance imaging (MRI) of pregnant women, brain segmentation is an important step to accurately assess the volumetric growth of the fetal brain, but technically challenging due to fetal and maternal motion and other physiological artifacts. Excellent scientists in Harvard Medical School began to demonstrate the feasibility of automatic segmentation of fetal brain using convolutional neural network (CNN) which is one of major deep learning technologies for image analyses [1]. Since last year, I have initiated a new...