Affiliations: [a] College of Biomedical Engineering, Sichuan University, Chengdu, China
| [b] Department of US, General Hospital of Western Theater Command, Chengdu, China
| [c] Haihong Intellimage Medical Technology (Tianjin) Co., Ltd, Tianjin, China
Correspondence:
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Corresponding author: Jiangli Lin, College of Biomedical Engineering, Sichuan University, Chengdu, 610065, China. E-mail: linjlscu@163.com.
Abstract: BACKGROUND:Standard planes (SPs) are crucial for the diagnosis of fetal brain malformation. However, it is very time-consuming and requires extensive experiences to acquire the SPs accurately due to the large difference in fetal posture and the complexity of SPs definitions. OBJECTIVE:This study aims to present a guiding approach that could assist sonographer to obtain the SPs more accurately and more quickly. METHODS:To begin with, sonographer uses the 3D probe to scan the fetal head to obtain 3D volume data, and then we used affine transformation to calibrate 3D volume data to the standard body position and established the corresponding 3D head model in ‘real time’. When the sonographer uses the 2D probe to scan a plane, the position of current plane can be clearly show in 3D head model by our RLNet (regression location network), which can conduct the sonographer to obtain the three SPs more accurately. When the three SPs are located, the sagittal plane and the coronal planes can be automatically generated according to the spatial relationship with the three SPs. RESULTS:Experimental results conducted on 3200 2D US images show that the RLNet achieves average angle error of the transthalamic plane was 3.91±2.86°, which has a obvious improvement compared other published data. The automatically generated coronal and sagittal SPs conform the diagnostic criteria and the diagnostic requirements of fetal brain malformation. CONCLUSIONS:A guiding scanning method based deep learning for ultrasonic brain malformation screening is firstly proposed and it has a pragmatic value for future clinical application.
