Abstract #4355

Amniotic Fluid Segmentation using Convolutional Neural Networks

Alejo Costanzo^1,2, Birgit Ertl-Wagner^3,4, and Dafna Sussman^1,5,6

¹Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada, ²Institute for Biomedical Engineering, Science and Technology, Toronto Metropolitan University and St. Michael’s Hospital, Toronto, ON, Canada, ³Division of Neuroradiology, The Hospital for Sick Children, Toronto, ON, Canada, ⁴Department of Medical Imaging, Faculty of Medicine, University of Toronto, Toronto, ON, Canada, ⁵Institute for Biomedical Engineering, Science and Technology, Toronto Metropolitan University and St. Michael’s Hospital, Toronto, ON, Canada, ⁶Department of Obstetrics and Gynecology, Faculty of Medicine, University of Toronto, Toronto, ON, Canada

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Segmentation, Convolutional Neural NetworkAmniotic 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.

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