2019/11/03 by Zahra Sobhaninia, Sobhaninia, Zahra, Ali Emami +5
Medicine · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Fetal and Pediatric Neurological Disorders #Image and Video Processing (eess.IV) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1911.00908
openalex publication_date 2019/11/03 · openalex created_date 2021/06/07 · openalex updated_date 2026/07/28
One of the routine examinations that are used for prenatal care in many\ncountries is ultrasound imaging. This procedure provides various information\nabout fetus health and development, the progress of the pregnancy and, the\nbaby's due date. Some of the biometric parameters of the fetus, like fetal head\ncircumference (HC), must be measured to check the fetus's health and growth. In\nthis paper, we investigated the effects of using multi-scale inputs in the\nnetwork. We also propose a light convolutional neural network for automatic HC\nmeasurement. Experimental results on an ultrasound dataset of the fetus in\ndifferent trimesters of pregnancy show that the segmentation accuracy and HC\nevaluations performed by a light convolutional neural network are comparable to\ndeep convolutional neural networks. The proposed network has fewer parameters\nand requires less training time.\n