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.

中文翻译：

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使用深度卷积神经网络的胎儿脑超声图像的计算机辅助诊断。

目的

胎儿脑部异常是一些最常见的先天畸形，可能与综合征和染色体畸形有关，并可能导致神经发育迟缓和智力低下。早期产前脑部异常的检测对于告知临床管理途径和为父母咨询至关重要。这项研究的目的是开发针对五种常见胎儿脑部异常的计算机辅助诊断算法，这可以为医生进行产前神经超声检查中脑部异常检测提供帮助。

方法

我们应用分类器将胎儿脑部标准平面（脑室和小脑）的图像分类为正常或异常。分类器由图像级标记图像训练。第一步，从超声图像中分割出颅脑区域。然后，将这些细分分为四类。最后，通过类别激活图定位异常图像中的病变。

结果

我们在胎儿大脑超声图像的真实临床数据集上评估了我们的算法。我们观察到，所提出的方法在颅脑区域分割上的Dice得分为0.942，分类时的平均F1得分为0.96，病变部位的平均IOU为0.497。

结论

我们提出了基于深度卷积神经网络的胎儿脑超声图像的计算机辅助诊断算法。我们的算法可潜在地应用于诊断辅助，有望帮助初级医生做出临床决策并减少胎儿脑部异常的假阴性。