Computer-aided diagnosis for fetal brain ultrasound images using deep convolutional neural networks
Abstract
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.
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Xie et al. and Sahli et al. reported a method for classifying US images into a binary system of 'normal' and 'abnormal' cases.
“Researchers Xie et al. [6,93] and Sahli et al. [94] reported a method for classifying US images into a binary system of ‘normal’ and ‘abnormal’ cases”
Xie et al. additionally localized the structural lesions, which lead the algorithm to declare it 'abnormal' and thus recommend the clinician to recheck the labeled area.
“in which Xie et al. additionally localized the structural lesions, which lead the algorithm to declare it ‘abnormal’ and thus recommend the clinician to recheck the labeled area.”
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- Authors
- Xie, B., Lei, T., Wang, N., Cai, H., Xian, J., He, M., Zhang, L., & Xie, H.
- Affiliations
- South China University of Technology (CN)
- The First Affiliated Hospital, Sun Yat-sen University (CN)
- Corresponding author
- Xie, Hongning
- Venue
- International Journal of Computer Assisted Radiology and Surgery
- Published
- 2020-06-02
- Volume
- 15
- Issue
- 8
- Pages
- 1303-1312
- ISSN
- 1861-6410 (ISSN-L)
- 1861-6429
- OpenAlex
- W3029526682
- Cited by
- 65
- References
- 33
- Citations by year
- 2026: 8 · 2025: 20 · 2024: 13 · 2023: 15 · 2022: 6 · 2021: 3
- FWCI
- 2.33
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Computer Assisted Radiology and Surgery (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (81571687, 61771007)
- Science and Technology Development Plan of Guangdong Province (2017A020214013, 2017B020226004)
- Health and Medical Collaborative Innovation Project of Guangzhou City (201803010021)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Fetal and Pediatric Neurological Disorders
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- fetal brain anomalies, prenatal neurosonography, computer-aided diagnosis, deep convolutional neural networks, lesion localization, class activation mapping, congenital malformations, image-level classification
- MeSH terms
- Algorithms, Brain, Brain Diseases, Diagnosis, Computer-Assisted, Female, Humans, Image Processing, Computer-Assisted, Pregnancy, Ultrasonography, Prenatal, Neural Networks, Computer
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