Ultrasonographic Segmentation of Fetal Lung with Deep Learning
作者单位：Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Department of Medical Ultrasound Fudan University Shanghai Cancer Center Department of Oncology Shanghai Medical College Fudan University School of Mechanical Engineering Northwestern Polytechnical University Center for Medical Ultrasound Nanjing Medical University Affiliated Suzhou Hospital
会议日期：2021年
学科分类：1002[医学-临床医学] 100211[医学-妇产科学] 10[医学]
关 键 词：Fetal Lung Fetal Heart Ultrasound Image Segmentation Deep Learning
摘 要：The morbidity and mortality of the fetus is related closely with the neonatal respiratory morbidity, which was caused by the immaturity of the fetal lung primarily. The amniocentesis has been used in clinics to evaluate the maturity of the fetal lung, which is invasive, expensive and time-consuming. Ultrasonography has been developed to examine the fetal lung quantitatively in the past decades as a non-invasive method. However, the contour of the fetal lung required by existing studies was delineated in manual. An automated segmentation approach could not only improve the objectiveness of those studies, but also offer a quantitative way to monitor the development of the fetal lung in terms of morphological parameters based on the segmentation. In view of this, we proposed a deep learning model for automated fetal lung segmentation and measurement. The model was constructed based on the U-Net. It was trained by 3500 data sets augmented from 250 ultrasound images with both the fetal lung and heart manually delineated, and then tested on 50 ultrasound data sets. With the proposed method, the fetal lung and cardiac area were automatically segmented with the accuracy, average IoU, sensitivity and precision being 0.98, 0.79, 0.881 and 0.886, respectively.