Costanzo Alejo, Ertl-Wagner Birgit, Sussman Dafna
Department of Electrical, Computer and Biomedical Engineering, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada.
Institute for Biomedical Engineering, Science and Technology (iBEST), Toronto Metropolitan University and St. Michael's Hospital, Toronto, ON M5B 1T8, Canada.
Bioengineering (Basel). 2023 Jun 30;10(7):783. doi: 10.3390/bioengineering10070783.
Amniotic Fluid Volume (AFV) is a crucial fetal biomarker when diagnosing specific fetal abnormalities. This study proposes a novel Convolutional Neural Network (CNN) model, AFNet, for segmenting amniotic fluid (AF) to facilitate clinical AFV evaluation. AFNet was trained and tested on a manually segmented and radiologist-validated AF dataset. AFNet outperforms ResUNet++ by using efficient feature mapping in the attention block and transposing convolutions in the decoder. Our experimental results show that AFNet achieved a mean Intersection over Union (mIoU) of 93.38% on our dataset, thereby outperforming other state-of-the-art models. While AFNet achieves performance scores similar to those of the UNet++ model, it does so while utilizing merely less than half the number of parameters. By creating a detailed AF dataset with an improved CNN architecture, we enable the quantification of AFV in clinical practice, which can aid in diagnosing AF disorders during gestation.
羊水量（AFV）是诊断特定胎儿异常时的一种关键胎儿生物标志物。本研究提出了一种新型卷积神经网络（CNN）模型AFNet，用于分割羊水（AF）以促进临床羊水量评估。AFNet在一个经过手动分割且经放射科医生验证的AF数据集中进行训练和测试。AFNet通过在注意力模块中使用高效特征映射以及在解码器中使用转置卷积，性能优于ResUNet++。我们的实验结果表明，AFNet在我们的数据集中实现了93.38%的平均交并比（mIoU），从而优于其他现有最先进模型。虽然AFNet实现的性能分数与UNet++模型相似，但它仅使用了不到一半的参数数量。通过创建一个具有改进CNN架构的详细AF数据集，我们能够在临床实践中对羊水量进行量化，这有助于在妊娠期诊断羊水疾病。
