An automated framework for image classification and segmentation of fetal ultrasound images for gestational age estimation

Juan C. Prieto; Hina Shah; Alan J. Rosenbaum; Xiaoning Jiang; Patrick Musonda; Joan T. Price; Elizabeth M. Stringer; Bellington Vwalika; David M. Stamilio; Jeffrey S. A. Stringer

doi:10.1117/12.2582243

15 February 2021 An automated framework for image classification and segmentation of fetal ultrasound images for gestational age estimation

Juan C. Prieto, Hina Shah, Alan J. Rosenbaum, Xiaoning Jiang, Patrick Musonda, Joan T. Price, Elizabeth M. Stringer, Bellington Vwalika, David M. Stamilio, Jeffrey S. A. Stringer

Author Affiliations +

Proceedings Volume 11596, Medical Imaging 2021: Image Processing; 115961N (2021) https://doi.org/10.1117/12.2582243
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

Accurate assessment of fetal gestational age (GA) is critical to the clinical management of pregnancy. Industrialized countries rely upon obstetric ultrasound (US) to make this estimate. In low- and middle- income countries, automatic measurement of fetal structures using a low-cost obstetric US may assist in establishing GA without the need for skilled sonographers. We present a technique for generating raw images suitable for model training by removing caliper and text annotation and describe a fully automated pipeline for image classification, segmentation, and structure measurement to estimate the GA. he resulting framework achieves an average accuracy of 93% in classification tasks, a mean Intersection over Union accuracy of 0.91 during segmentation tasks, and a mean measurement error of 1.89 centimeters, finally leading to a 1.4 day mean average error in the predicted GA compared to expert sonographer GA estimate using the Hadlock equation.

Conference Presentation

Citation Download Citation

Juan C. Prieto, Hina Shah, Alan J. Rosenbaum, Xiaoning Jiang, Patrick Musonda, Joan T. Price, Elizabeth M. Stringer, Bellington Vwalika, David M. Stamilio, and Jeffrey S. A. Stringer "An automated framework for image classification and segmentation of fetal ultrasound images for gestational age estimation", Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 115961N (15 February 2021); https://doi.org/10.1117/12.2582243

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available