Automatic fetal biometry prediction using a novel deep convolutional network architecture. (August 2021)

• Record Type: Journal Article
• Authors: Ghelich Oghli, Mostafa; Shabanzadeh, Ali; Moradi, Shakiba; Sirjani, Nasim; Gerami, Reza; Ghaderi, Payam; Sanei Taheri, Morteza; Shiri, Isaac; Arabi, Hossein; Zaidi, Habib
• Is Part Of: Physica medica. Volume 88(2021)
• Page Start: 127
• Page End: 137
• Publication Date: 2021-08
• Subjects: Fetal biometry -- Ultrasound imaging -- Deep learning -- Convolutional neural network -- Image classification; Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2021.06.020
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: A convolutional neural network is proposed for fetal biometry measurement. MFP-Unet architecture is modified and attention gates are incorporated. A novel algorithm is also developed for detection femur images. A system is proposed for determining the second channel of the network. The performance of the model is evaluated using public and prepared datasets. Abstract: Purpose: Fetal biometric measurements face a number of challenges, including the presence of speckle, limited soft-tissue contrast and difficulties in the presence of low amniotic fluid. This work proposes a convolutional neural network for automatic segmentation and measurement of fetal biometric parameters, including biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), and femur length (FL) from ultrasound images that relies on the attention gates incorporated into the multi-feature pyramid Unet (MFP-Unet) network. Methods: The proposed approach, referred to as Attention MFP-Unet, learns to extract/detect salient regions automatically to be treated as the object of interest via the attention gates. After determining the type of anatomical structure in the image using a convolutional neural network, Niblack's thresholding technique was applied as pre-processing algorithm for head and abdomen identification, whereas a novel algorithm was used for femur extraction. A publicly-available dataset (HC18 grand-challenge) and clinical data of 1334 subjects were utilized for training and evaluation of the Attention MFP-Unet algorithm. Results: Dice similarity coefficient (DSC), hausdorff distance (HD), percentage of good contours, the conformity coefficient, and average perpendicular distance (APD) were employed for quantitative evaluation of fetal anatomy segmentation. In addition, correlation analysis, good contours, and conformity were employed to evaluate the accuracy of the biometry predictions. Attention MFP-Unet achieved 0.98, 1.14 mm, 100%, 0.95, and 0.2 mm for DSC, HD, good contours, conformity, and APD, respectively. Conclusions: Quantitative evaluation demonstrated the superior performance of the Attention MFP-Unet compared to state-of-the-art approaches commonly employed for automatic measurement of fetal biometric parameters.