Abstract The transthalamic plane of fetal is used to measure the biparietal diameter and head circumference of the fetus, and these two measurement parameters play an important role in predicting the fetal weight. Clinically, the plane has always been manually acquired by the ultrasound doctor, and the quality of the manually obtained plane is highly dependent on the clinical work experience of the doctor, which is time consuming, and the poor image quality for plane often happen. In order to overcome the problem of manual acquisition, we proposed a novel method for the quality assessment of fetal head ultrasound images based onfaster region-based convolutional neural networks (faster R-CNN), aiming to help doctors automatically, quickly and accurately obtain the standard transthalamic plane. Frist, we set up an evaluation protocol with the team of ultrasound experts and build a database of fetal head ultrasound images through data-enhanced methods. Second, faster R-CNN could learn from the training data to extract useful features, and through the use of joint training and alternative optimization, so that the regional proposal networks (RPN) module and fast R-CNN module shared the convolution layer features and built a complete end-to-end CNN object detection model to detect the key anatomical structures. Finally, the transthalamic plane was automatically scored by the results of the detected anatomical structure, and according to the score results, it was automatically determined whether the plane was a standard one. We collected 513 ultrasound planes, 80% was used as a training dataset and 20% as a test dataset. Our method could accurately locate the five anatomical structures of the transthalamic plane with an average accuracy of 80.7% and the examination time of each ultrasound image was approximately 0.27 s, which indicated that it was feasible to perform automated quality control of fetal head ultrasound images by the proposed method.
Lin Zehui,Lei Baiying,Jiang Feng, et al. Quality Assessment of Fetal Head Ultrasound Images Based on Faster R-CNN[J]. Chinese Journal of Biomedical Engineering, 2019, 38(4): 392-400.
[1] Li Jing, Ni Dong, Li Shengli, et al. The automatic ultrasound measurement of fetal head circumference [J]. Journal of Shenzhen University Science & Engineering, 2014, 31(5): 455-463. [2] Wu Lingyun, Cheng Jiezhi, Li Shengli, et al. FUIQA: fetal ultrasound image quality assessment with deep convolutional networks [J]. IEEE Transactions on Cybernetics, 2017, 47(5): 1336-1349. [3] He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al. Deep residual learning for image recognition [C] // IEEE Conference on Computer Vision and Pattern Recognition. Lasvegas: IEEE, 2016: 770-778. [4] Redmon Joseph, Farhadi Ali. YOLO9000: better, faster, stronger [C] // IEEE Conference on Computer Vision and Pattern Recognition. Hawaii: IEEE, 2017: 7263-7271. [5] Ni Dong., Yang Xin., Chen Xin., et al. Standard plane localization in ultrasound by radial component model and selective search [J]. Ultrasound in Medicine & Biology, 2014, 40(11): 2728-2742. [6] Zhang Qi, Xiao Yang, Dai Wei, et al. Deep learning based classification of breast tumors with shear-wave elastography [J]. Ultrasonics, 2016. 72: 150-157. [7] Zhang Qi, Xiao Yang, Suo Jingfeng, et al. Sonoelastomics for breast tumor classification: a radiomics approach with clustering-based feature selection on sonoelastography [J]. Ultrasound in Medicine & Biology, 2017. 43(5): 1058-1069. [8] Sundaresan Vaanathi, Bridge Christopher P., Ioannou Christos, et al. Automated characterization of the fetal heart in ultrasound images using fully convolutional neural networks [C] // IEEE International Symposium on Biomedical Imaging. Melbourne: IEEE, 2017: 671-674. [9] Chen Hao., Ni Dong., Qin Jing., et al. Standard plane localization in fetal ultrasound via domain transferred deep neural networks [J]. IEEE J Biomed Health Inform, 2015, 19(5): 1627-1636. [10] Cavallaro Angelo, Ash Stephen T, Napolitano Raffaele, et al. Quality control of ultrasound for fetal biometry: Results from the INTERGROWTH-21st project [J]. Ultrasound in Obstetrics & Gynecology, 2018, 52(3): 332-339. [11] Li Shengli, Chen Xiulan, Yao Yuan, et al. Qualiyt control of training prenatal ultrasound doctors in advanced training [J]. Medical Ultrasound of Chinese Journal, 2009, 6(4): 14-17. [12] Namburete Anail, Xie Weidi., Yaqub Mohammad., et al. Fully-automated alignment of 3D fetal brain ultrasound to a canonical reference space using multi-task learning [J]. Medical Image Analysis, 2018, 46: 1-14. [13] 李胜利. 胎儿畸形产前超声与病理解剖图谱[M].北京:人民军医出版社, 2013. [14] He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition [J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2015, 37(9): 1904-1916. [15] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition [J]. arXiv preprint arXiv, 2014,1409.1556. [16] Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation [C] // IEEE Conference on Computer Vision and Pattern Recognition. Columbus: IEEE, 2013: 580-587. [17] Girshick Ross. Fast R-CNN [C] // IEEE International Conference on Computer Vision. Santiago: IEEE, 2015: 1440-1448. [18] Redmon J, Divvala S, Girshick R, et al. You only look once: unified, real-time object detection [C] // IEEE Conference on Computer Vision and Pattern Recognition. Lasvegas: IEEE, 2016: 779-788. [19] Ren Shaoqing, He Kaiming, Girshick Ross, et al. Faster R-CNN: Towards real-time object detection with region proposal networks [J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2015, 39(6): 1137-1149. [20] Uijlings Jasper RR, Van de S, Koen EA, et al. Selective search for object recognition [J]. International Journal of Computer Vision, 2013, 104(2): 154-171. [21] Van de S, Koen EA, Uijlings Jasper RR, et al. Segmentation as selective search for object recognition [C] // IEEE International Conference on Computer Vision. Barcelona: IEEE, 2011: 1879-1886.
