DeepGA for automatically estimating fetal gestational age through ultrasound imaging.

Accurate estimation of gestational age (GA) is vital for identifying fetal abnormalities. Conventionally, GA is estimated by measuring the morphology of the cranium, abdomen, and femur manually and inputting them into the classic Hadlock formula to assess fetal growth. However, this procedure incurs considerable overhead and suffers from bias caused by the operators, yielding suboptimal estimations. To address this challenge, we develop an automatic DeepGA model to achieve fully automatic GA prediction in an end-to-end manner. Our model uses a deep segmentation model (DeepSeg) to accurately identify and segment three critical tissues, including the cranium, abdomen, and femur, in which their morphology is automatically extracted. After that, we are able to directly estimate the GA via a deep regression model (DeepReg). We evaluate DeepGA on a large dataset, including 10,413 ultrasound images from 7113 subjects. It achieves superior performance over the traditional measurement approach, with a mean absolute estimation error (MAE) of 5 days. Our DeepGA model is a novel automatic solution on the basis of artificial intelligence learning that can help radiologists improve the performance of GA estimation in various clinical scenarios, thereby enhancing the efficiency of prenatal examinations.

Automatic Measurements of Fetal Lateral Ventricles in 2D Ultrasound Images Using Deep Learning.

Measurement of the width of fetal lateral ventricles (LVs) in prenatal ultrasound (US) images is essential for antenatal neuronographic assessment. However, the manual measurement of LV width is highly subjective and relies on the clinical experience of scanners. To deal with this challenge, we propose a computer-aided detection framework for automatic measurement of fetal LVs in two-dimensional US images. First, we train a deep convolutional network on 2,400 images of LVs to perform pixel-wise segmentation. Then, the number of pixels per centimeter (PPC), a vital parameter for quantifying the caliper in US images, is obtained via morphological operations guided by prior knowledge. The estimated PPC, upon conversion to a physical length, is used to determine the diameter of the LV by employing the minimum enclosing rectangle method. Extensive experiments on a self-collected dataset demonstrate that the proposed method achieves superior performance over manual measurement, with a mean absolute measurement error of 1.8 mm. The proposed method is fully automatic and is shown to be capable of reducing measurement bias caused by improper US scanning.

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

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.