Title :
Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks
Author :
Hao Chen ; Dong Ni ; Jing Qin ; Shengli Li ; Xin Yang ; Tianfu Wang ; Pheng Ann Heng
Author_Institution :
Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong, Hong Kong, China
Abstract :
Automatic localization of the standard plane containing complicated anatomical structures in ultrasound (US) videos remains a challenging problem. In this paper, we present a learning-based approach to locate the fetal abdominal standard plane (FASP) in US videos by constructing a domain transferred deep convolutional neural network (CNN). Compared with previous works based on low-level features, our approach is able to represent the complicated appearance of the FASP and hence achieve better classification performance. More importantly, in order to reduce the overfitting problem caused by the small amount of training samples, we propose a transfer learning strategy, which transfers the knowledge in the low layers of a base CNN trained from a large database of natural images to our task-specific CNN. Extensive experiments demonstrate that our approach outperforms the state-of-the-art method for the FASP localization as well as the CNN only trained on the limited US training samples. The proposed approach can be easily extended to other similar medical image computing problems, which often suffer from the insufficient training samples when exploiting the deep CNN to represent high-level features.
Keywords :
biomedical ultrasonics; image classification; learning (artificial intelligence); medical image processing; neural nets; object detection; obstetrics; FASP localization; US videos; automatic standard plane localization; classification performance; domain transferred deep convolutional neural network; fetal abdominal standard plane; fetal ultrasound; high-level features; learning-based approach; low-level features; medical image computing problems; natural images; overfitting problem; task-specific CNN; transfer learning strategy; ultrasound videos; Biomedical imaging; Dictionaries; Feature extraction; Informatics; Standards; Training; Videos; Convolutional neural network (CNN); Ultrasound; convolutional neural network; deep learning; domain transfer; knowledge transfer; standard plane; ultrasound (US);
Journal_Title :
Biomedical and Health Informatics, IEEE Journal of
DOI :
10.1109/JBHI.2015.2425041