Print Email Facebook Twitter Automated Carnegie Staging of the Human Embryo in 3D Ultrasound using Deep Learning Title Automated Carnegie Staging of the Human Embryo in 3D Ultrasound using Deep Learning Author Niemantsverdriet, Ruben (TU Delft Mechanical, Maritime and Materials Engineering) Contributor Vos, F.M. (mentor) Bastiaansen, Wietske (mentor) Klein, Stefan (mentor) Rousian, Melek (mentor) Degree granting institution Delft University of Technology Programme Biomedical Engineering | Medical Physics Date 2024-02-27 Abstract The periconceptional period, encompassing the embryonic phase, is a critical window where a majority of reproductive failures, pregnancy complications, and adverse pregnancy outcomes arise. The Carnegie staging system comprises 23 stages which are based on embryonic morphological development. This allows for the assessment of normal and abnormal embryonic development during this critical period. In-utero Carnegie staging using three-dimensional (3D) ultrasound scans visualized with virtual reality offers valuable insights but is currently a time-consuming manual process. To address this, we propose a deep learning approach for Carnegie staging in 3D ultrasound scans. We used a dataset comprising 1413 3D ultrasound scans from the Rotterdam Periconceptional Cohort, annotated with Carnegie stages spanning from stages 13 to 23, including fetal subjects. Various training strategies were explored. We compared a metric regression approach, which considers the ordered nature of the Carnegie stages by treating the Carnegie stages as a continuous variable, with a multi-class classification approach, treating stages as independent categories. Additionally, we evaluated the influence of using a loss function accommodating the categorical nature of the Carnegie stages in the metric regression approach and examined the impact of incorporating embryonic size in the model input. Ultimately, a regression approach using the Mean Squared Error (MSE) loss function emerged as the optimal choice.This model achieved a classification accuracy of 0.59 and a Root Mean Squared Error (RMSE) of 0.62 on the test set. This performance is comparable to an intermediate human rater, which achieved an accuracy of 0.63 and a RMSE of 0.65. Our findings represent a significant step towards the development of an automated Carnegie staging method, offering the potential for a more comprehensive evaluation of the critical embryonic phase in the clinic. Subject Machine LearningDeep Learningembryonic development3D Ultrasound To reference this document use: http://resolver.tudelft.nl/uuid:5cdae823-66c5-40e2-b624-29da6a3d8456 Part of collection Student theses Document type master thesis Rights © 2024 Ruben Niemantsverdriet Files PDF BME_Master_thesis_Ruben_N ... rdriet.pdf 13.6 MB Close viewer /islandora/object/uuid:5cdae823-66c5-40e2-b624-29da6a3d8456/datastream/OBJ/view