Amniotic Fluid Segmentation using Convolutional Neural Networks

Abstract

Amniotic Fluid Volume (AFV) is an important fetal biomarker when diagnosing certain fetal abnormalities. We aim to implement a novel Convolutional Neural Network (CNN) model for amniotic fluid (AF) segmentation which can facilitate clinical AFV evaluation. The model, called AFNet was trained and tested on a radiologist–validated AF dataset. AFNet improves upon ResUNet++ through the efficient feature mapping in the attention block, and transpose convolutions in the decoder. Experimental results show that our AFNet model achieved a 93.38% mean Intersection over Union (mIoU) on our dataset. We further demonstrate that AFNet outperforms state-of-the-art models while maintaining a low model size.