Using deep learning algorithms to classify fetal brain ultrasound images as normal or abnormal.
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Abstract
To evaluate the feasibility of classifying sonographic images of fetal brain images taken in standard axial planes as normal or abnormal, using deep learning algorithms.
A total of 92748 prenatal examinations were used in the study. After inclusion and exclusion, 10251 normal and 2529 abnormal pregnancies were included. Abnormal cases were confirmed by neonatal ultrasound, follow-up examination or autopsy. After a series of data pretraining processes, 15372 normal and 14047 abnormal fetal brain images were included. They were divided into training and test datasets (on a case level rather than on an image level), at a ratio of approximately 8:2. Training data were used to train the algorithms to classify images as normal or abnormal, and the accuracy was then tested on the test datasets. The algorithms were trained for three purposes: image segmentation along fetal skull, classifying the image and localizing the lesion. Performance of segmentation was assessed using precision, recall, and Dice’s coefficient (DICE), calculated to measure the extent of overlap between human-labeled and machine-segmented regions. Sensitivity and specificity were calculated for classification accuracy assessment. Additionally, for abnormal images, how well a lesion was localized was determined.
Segmentation precision, recall and DICE were 97.9%, 90.9% and 94.1%, respectively. For classification the overall accuracy was 96.3%. The sensitivity and specificity for abnormal images were 96.9% and 95.9%, respectively. The area under the receiver operating characteristic curve was 0.989 (95% CI: 0.986-0.991). For 2491 abnormal fetal brain images, the lesions were precisely, closely and irrelevantly located in 61.6% (1535/2491), 24.7% (614/2491) and 13.7% (342/2491), respectively.
Deep learning algorithms could be trained for segmentation and classification of normal and abnormal images and provide heat maps for lesion localization. This study laid a foundation for further research on the differential diagnosis of fetal intracranial abnormalities. This article is protected by copyright. All rights reserved.
This article is protected by copyright. All rights reserved.