Proceedings of the AAAI Conference on Artificial Intelligence • Vol 37 • No 13
AmnioML: Amniotic Fluid Segmentation and Volume Prediction with Uncertainty Quantification
June 2023 • Daniel Csillag, Lucas Monteiro Paes, Thiago Ramos, João Vitor Romano, R. B. Schüller, Roberto B. Seixas, Roberto I. Oliveira, Paulo Orenstein
Accurately predicting the volume of amniotic fluid is fundamental to assessing pregnancy risks, though the task usually requires many hours of laborious work by medical experts. In this paper, we present AmnioML, a machine learning solution that leverages deep learning and conformal prediction to output fast and accurate volume estimates and segmentation masks from fetal MRIs with Dice coefficient over 0.9. Also, we make available a novel, curated dataset for fetal MRIs with 853 exams and benchmark the performance…