PDF(Pubmed)
Abstract：
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.
摘要：
羊水体积（AFV）是诊断特定胎儿异常时的关键胎儿生物标志物。本研究提出了一种新的卷积神经网络(CNN)模型，AFNet,用于分割羊水（AF）以促进临床AFV评估。在手动分割和放射科医师验证的AF数据集上训练和测试AFNet。AFNet通过在注意块中使用有效的特征映射并在解码器中转置卷积来优于ResUNet++。我们的实验结果表明，AFNet在我们的数据集上实现了93.38%的平均交集。从而胜过其他最先进的模型。虽然AFNet的性能得分与UNet++模型相似，它这样做，同时只利用不到一半的参数数量。通过使用改进的CNN架构创建详细的AF数据集，我们能够在临床实践中量化AFV，这可以帮助诊断在妊娠期房颤疾病。
