Leveraging Shape and Spatial Information for Spontaneous Preterm Birth Prediction

Paraskevas Pegios*, Emilie Pi Fogtmann Sejer, Manxi Lin, Zahra Bashir, Morten Bo Søndergaard Svendsen, Mads Nielsen, Eike Petersen, Anders Nymark Christensen, Martin Tolsgaard, Aasa Feragen

*Corresponding author af dette arbejde


Spontaneous preterm birth prediction from transvaginal ultrasound images is a challenging task of profound interest in gynecological obstetrics. Existing works are often validated on small datasets and may lack validation of model calibration and interpretation. In this paper, we present a comprehensive study of methods for predicting preterm birth from transvaginal ultrasound using a large clinical dataset. We propose a shape- and spatially-aware network that leverages segmentation predictions and pixel spacing information as additional input to enhance predictions. Our model demonstrates competitive performance on our benchmark, providing additional interpretation and achieving the highest performance across both clinical and machine learning baselines. Through our evaluation, we provide additional insights which we hope may lead to more accurate predictions of preterm births going forwards.

TitelSimplifying Medical Ultrasound - 4th International Workshop, ASMUS 2023, Held in Conjunction with MICCAI 2023, Proceedings
RedaktørerBernhard Kainz, Johanna Paula Müller, Bernhard Kainz, Alison Noble, Julia Schnabel, Bishesh Khanal, Thomas Day
Antal sider11
ForlagSpringer Science and Business Media Deutschland GmbH
ISBN (Trykt)9783031445200
StatusUdgivet - 2023
Begivenhed4th International Workshop of Advances in Simplifying Medical Ultrasound, ASMUS 2023 - Vancouver, Canada
Varighed: 8 okt. 20238 okt. 2023


Konference4th International Workshop of Advances in Simplifying Medical Ultrasound, ASMUS 2023
NavnLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vol/bind14337 LNCS


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