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Automated fetal lateral ventricular width estimation from prenatal ultrasound based on deep learning algorithms
  • +8
  • Ruizhi Liu,
  • Bin Zou,
  • Hongyang Zhang,
  • Jingyu Ye,
  • Cong Han,
  • nianji zhan,
  • Ying Yang,
  • Hongguo Zhang,
  • Fang Chen,
  • Shucheng Hua,
  • Jian Guo
Ruizhi Liu
Jilin University First Hospital
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Bin Zou
BGI-Shenzhen
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Hongyang Zhang
Jilin University First Hospital
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Jingyu Ye
BGI-Shenzhen
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Cong Han
Jilin University First Hospital
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nianji zhan
BGI-Shenzhen
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Ying Yang
BGI-Shenzhen
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Hongguo Zhang
Jilin University First Hospital
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Fang Chen
BGI-Shenzhen
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Shucheng Hua
Jilin University First Hospital
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Jian Guo
BGI-Shenzhen
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Abstract

Ventriculomegaly (VM) is the medical term used to describe enlargement of the lateral ventricles to a level of 10 mm or more, which is the most frequent sign of possible CNS abnormality detected on prenatal ultrasound. In this paper, we aim to evaluate the feasibility of CNN-based DL algorithms predicting the fetal lateral ventricular width from prenatal ultrasound images. The data was collected from 626 pregnant women with gestational age between 22 to 26 weeks. 3456 brain images were picked out from all 49222 stored freeze-frame images. 2304 transventricular (TV) or transthalamic (TT) plane images were further picked out and the brain regions were detected and extracted. 1431 TV-TT planes had known lateral ventricular width. The mean absolute error (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 only 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. We also implemented heat maps to provide evidence that our regression model predicting the lateral ventricular width was based on the anatomical structure of lateral ventricular. The results shown that the regression model can locate the lateral ventricular region of images with large lateral ventricular width successfully and then predict its width based on this region.