Computer-aided diagnosis for fetal brain ultrasound images using deep convolutional neural networks

• Published in: International journal for computer assisted radiology and surgery Vol. 15; no. 8; pp. 1303 - 1312
• Main Authors: Xie, Baihong, Lei, Ting, Wang, Nan, Cai, Hongmin, Xian, Jianbo, He, Miao, Zhang, Lihe, Xie, Hongning
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.08.2020; Springer Nature B.V
• Subjects: Abnormalities; Algorithms; Artificial neural networks; Brain; Brain - diagnostic imaging; Brain Diseases - diagnostic imaging; Classifiers; Computer Imaging; Computer Science; Diagnosis; Diagnosis, Computer-Assisted - methods; Female; Health Informatics; Humans; Image classification; Image Processing, Computer-Assisted - methods; Image segmentation; Imaging; Mapping; Medical imaging; Medicine; Medicine & Public Health; Neural networks; Neural Networks, Computer; Original Article; Pattern Recognition and Graphics; Physicians; Pregnancy; Radiology; Surgery; Ultrasonic imaging; Ultrasonography, Prenatal - methods; Ultrasound; Vision; Prenatal ultrasound images; Deep convolutional neural network; Fetal brain abnormalities; Craniocerebral segmentation; Computer-aided diagnosis
• ISSN: 1861-6410; 1861-6429; 1861-6429
• DOI: 10.1007/s11548-020-02182-3

Purpose Fetal brain abnormalities are some of the most common congenital malformations that may associated with syndromic and chromosomal malformations, and could lead to neurodevelopmental delay and mental retardation. Early prenatal detection of brain abnormalities is essential for informing clinical management pathways and consulting for parents. The purpose of this research is to develop computer-aided diagnosis algorithms for five common fetal brain abnormalities, which may provide assistance to doctors for brain abnormalities detection in antenatal neurosonographic assessment. Methods We applied a classifier to classify images of fetal brain standard planes (transventricular and transcerebellar) as normal or abnormal. The classifier was trained by image-level labeled images. In the first step, craniocerebral regions were segmented from the ultrasound images. Then, these segmentations were classified into four categories. Last, the lesions in the abnormal images were localized by class activation mapping. Results We evaluated our algorithms on real-world clinical datasets of fetal brain ultrasound images. We observed that the proposed method achieved a Dice score of 0.942 on craniocerebral region segmentation, an average F1-score of 0.96 on classification and an average mean IOU of 0.497 on lesion localization. Conclusion We present computer-aided diagnosis algorithms for fetal brain ultrasound images based on deep convolutional neural networks. Our algorithms could be potentially applied in diagnosis assistance and are expected to help junior doctors in making clinical decision and reducing false negatives of fetal brain abnormalities.