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| Fetal Facial Standard Plane Recognition via Deep Convolutional Neural Networks |
| Yu Zhen1, Wu Lingyun1, Ni Dong1, Chen Siping1, Li Shengli2, Wang Tianfu1*, Lei Baiying1* |
1(Schoolof Biomedical Engineering, Shenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, Guangdong, China)
2(Department of Ultrasound, Affiliated Shenzhen Maternal and Child Healthcare, Hospital of Nanfang Medical University, Shenzhen 518060, Guangdong, China) |
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Abstract The accurate recognition of fetal facial standard plane (FFSP) (i.e., axial, coronal and sagittal plane) from ultrasound (US) images is quite essential for routine US examination. Since the labor-intensive and subjective measurement is too time-consuming and unreliable, the development of the automatic FFSP recognition method is highly desirable. In this paper, we proposed to recognize FFSP using different depth CNN architectures (e.g., 8-layer and 16-layer). Specifically, we trained these models varied from depth to depth and mainly utilize two training strategy: 1) training the “CNN from scratch” with random initialization; 2) performing transfer learning strategy by fine-tuning ImageNet pre-trained CNN on our FFSP dataset. In our experiments, fetal gestational ages ranged typically from 20 to 36 weeks. Our training dataset contains 4849 images (i.e., 375 axial plane images, 257 coronal plane images, 405 sagittal plane images and 3812 non-FFSP images). Our testing dataset contained 2 418 images (i.e., 491 axial plane images, 127 coronal plane images, 174 sagittal plane images, and 1626 non-FFSP images). The experiment indicated that the strategy of transfer learning combined with CNN improving recognition accuracy by 9.29%. When CNN depth changes from 8 layer to 16 layer, it improves the recognition accuracy by 3.17%. The best recognition accuracy of our CNN model was 94.5%, which was 3.66% higher than our previous study. The effectiveness of deep CNN and transfer learning for FFSP recognition shows promising application for clinical diagnosis.
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Received: 08 June 2016
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