Discussion

Main Findings

This study is the first to predict the fetal lateral ventricular width using CNN-based DL algorithms. Our study shows that the scheme can automatically pick out brain images from all stored freeze-frame images. The sensitivity and specificity for brain images were 100% (376/376) and 96.9% (376/388), respectively. The scheme can recognize TV and TT planes and extract the brain regions. The sensitivity and specificity for TV-TT planes were 97.6% (205/210) and 99.5% (205/206), respectively. For the regression model, the MAE of the predicted lateral ventricular width was 1.01 mm. More than 65% test images had a MAE of less than 1 mm. If we used the 610 cases with lateral ventricular width less than 15 mm to train and test the model, the MAE was 0.54 mm and more than 82% test images had a MAE of less than 1 mm. The heat maps provide evidence that our regression model predicting the lateral ventricular width was based on the anatomical structure of lateral ventricular.

Strengths and Limitations

Many psychiatric and neurodevelopmental disorders are associated with enlargement of the lateral ventricles thought to have origins in prenatal brain development [47]. Moreover, VM is one of the most commonly detected fetal anomalies at the mid-trimester ultrasound (US) and occurs in up to 2 per 1000 births [39-40]. Therefore, recognizing this anomaly precisely and as early as possible is very important.
A previous study [35] used deep learning algorithms to classify fetal brain ultrasound images from standard axial planes as normal or abnormal. However, it is not suitable to combine together the ventriculomegaly cases and other CNS anomaly cases to train the classification model. One reason is that the number of VM cases is much higher than other CNS anomaly cases. In our dataset, from all the 22616 pregnant women, there are 90 VM cases (including 16 hydrocephalus cases) and only 24 other CNS anomaly. Another reason is that, predicting VM will focus only on the lateral ventricular region, while different other regions may be evaluated for other CNS anomaly prediction.
Moreover, the study [35] limited the ultrasound images as standard axial planes, while our study used all TV and TT planes. This is a real problem that many cases have no standard axial planes stored and inexperienced scanner may not be able to find out the desired standard planes. The authors claimed that 70690 out of 92748 cases contained no eligible standard axial neurosonographic planes and only about 16000 images can be used. In our study, the lateral ventricular width can be measured in more than half of the TV and TT planes and we have 1431 available images from 626 cases.
In this study, we did not have any scale reference in the images and the resolution of images were different. The ratio of the brain regions to the whole images were also not the same. To solve this problem, we detected and extracted the brain regions first and then resize the brain regions into a same size. Experiment results shown that this was a feasible way to mitigate the influence of these kinds of difference.
This study used only 626 pregnant women with gestational age between 22 to 26 weeks to train and test the modes. We got a MAE of 1.01 mm for the first experiment, which use all the 626 cases to train and test the regression model, and a MAE of 0.54 mm for the second experiment, which use the 610 cases with lateral ventricular width less than 15 mm to train and test the model. If we use more data, such as the data from the third trimester of pregnancy, to train the models, the MAE would potentially be reduced.
The lateral ventricular width is a continuous value. For ventriculomegaly the threshold is 10 mm. For our models, we recommend a smaller threshold like 9 mm or 8 mm. If the predicted lateral ventricular width is bigger than this value, doctors should pay attention to this fetal. A relatively small threshold can reduce false negative prediction, which may lead to serious consequence. However, false positive prediction is inevitable. In the first experiment, 53 images were predicted with lateral ventricular width bigger than 9 mm. Among them, 30 were actually bigger than 10 mm and the ground truth of other 23 images ranging from 8.3 mm to 0.99 mm. On the other hand, only a small fraction of fetuses has large lateral ventricular width, if our models can filter out most cases with small lateral ventricular width, the workload of doctors can be reduced hugely.

Interpretation

Although the lateral ventricular width is usually measured in TV planes, some doctors may measure this value in the TT plane or a transitionary plane between TV and TT planes. In our dataset, a considerable portion of TT planes were stored and used to measure the lateral ventricular width. Furthermore, if the lateral ventricular width is very large, it is usually hard to distinguish between TV and TT planes. For these reasons, we used TV and TT planes for lateral ventricular width estimation.
We built two models to predict the lateral ventricular width. The second one was to use the 610 cases with lateral ventricular width less than 15 mm, to train and test the model. The reason is that, severe fetal ventriculomegaly with lateral ventricular diameter >15 mm (also sometimes classified as fetal hydrocephalus) is unusual and their ultrasound images are much different from those of normal or mild fetal ventriculomegaly cases. This kind of cases can be detected using algorithms classifying fetal brain as normal or abnormal, such as study [35] did. Furthermore, after ignored these cases, the performance of the model improved remarkably.
After training the regression model to predict the lateral ventricular width, we generated heat maps and their corresponding overlay images for all test images. We found that, for the first experiment, all the heat maps were activated at the left-upper corner. We guess the model used the left-upper corner of each image to train something like a base value to lower the overall MSE. The final predicted lateral ventricular width combined the so-called base value with the value related to the lateral ventricular region. If the lateral ventricular width was small, the model might not detect the lateral ventricular region, and the final predicted lateral ventricular width would be only determined by the left-upper corner of the image. This was not very precision, but it was safe for images with small lateral ventricular width. It was similar for the second experiment that, the predicted lateral ventricular width of most images with small lateral ventricular width were based on other areas rather than the lateral ventricles.
It was worth noting that some images had markers on the lateral ventricles. Was it possible that the models localized the lateral ventricles and predicting lateral ventricular width using these markers? From the heat maps we can see that, some images with large lateral ventricular width and without markers were activated on the lateral ventricles, while some images with small lateral ventricular width and with markers were not activated on the lateral ventricles. We can conclude that the regression model predicting lateral ventricular width did not depend on the markers.