Journal
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Volume 22, Issue 3, Pages 874-885Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JBHI.2017.2705031
Keywords
Deep convolutional neural network; standard plane recognition; transfer learning; ultrasound image
Categories
Funding
- National Natural Science Foundation of China [81571758, 61571304, 61402296, 61427806]
- National Key Research and Develop Program [2016YFC0104703]
- Guangdong Medical [B2016094]
- Shenzhen Peacock Plan [KQTD2016053112051497]
- Shenzhen Key Basic Research Project [JCYJ20150525092940986, JCYJ20150525092940988]
- National Natural Science Foundation of Shenzhen University [827000197]
Ask authors/readers for more resources
Ultrasound imaging has become a prevalent examination method in prenatal diagnosis. Accurate acquisition of fetal facial standard plane (FFSP) is the most important precondition for subsequent diagnosis and measurement. In the past few years, considerable effort has been devoted to FFSP recognition using various hand-crafted features, but the recognition performance is still unsatisfactory due to the high intraclass variation of FFSPs and the high degree of visual similarity between FFSPs and other non-FFSPs. To improve the recognition performance, we propose a method to automatically recognize FFSP via a deep convolutional neural network (DCNN) architecture. The proposed DCNN consists of 16 convolutional layers with small 3 x 3 size kernels and three fully connected layers. A global average pooling is adopted in the last pooling layer to significantly reduce network parameters, which alleviates the overfitting problems and improves the performance under limited training data. Both the transfer learning strategy and a data augmentation technique tailored for FFSP are implemented to further boost the recognition performance. Extensive experiments demonstrate the advantage of our proposed method over traditional approaches and the effectiveness of DCNN to recognize FFSP for clinical diagnosis.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available