TY - GEN
T1 - Diffusion Models for Unsupervised Anomaly Detection in Fetal Brain Ultrasound
AU - Mykula, Hanna
AU - Gasser, Lisa
AU - Lobmaier, Silvia
AU - Schnabel, Julia A.
AU - Zimmer, Veronika
AU - Bercea, Cosmin I.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - Ultrasonography is an essential tool in mid-pregnancy for assessing fetal development, appreciated for its non-invasive and real-time imaging capabilities. Yet, the interpretation of ultrasound images is often complicated by acoustic shadows, speckle, and other artifacts that obscure crucial diagnostic details. To address these challenges, our study presents a novel unsupervised anomaly detection framework specifically designed for fetal ultrasound imaging. This framework incorporates gestational age filtering, precise identification of fetal standard planes, and targeted segmentation of brain regions to enhance diagnostic accuracy. Furthermore, we introduce the use of denoising diffusion probabilistic models in this context, marking a significant innovation in detecting previously unrecognized anomalies. We rigorously evaluated the framework using various diffusion-based anomaly detection methods, noise types, and noise levels. Notably, AutoDDPM emerged as the most effective, achieving an area under the precision-recall curve of 79.8% in detecting anomalies. This advancement holds promise for improving the tools available for nuanced and effective prenatal diagnostics.
AB - Ultrasonography is an essential tool in mid-pregnancy for assessing fetal development, appreciated for its non-invasive and real-time imaging capabilities. Yet, the interpretation of ultrasound images is often complicated by acoustic shadows, speckle, and other artifacts that obscure crucial diagnostic details. To address these challenges, our study presents a novel unsupervised anomaly detection framework specifically designed for fetal ultrasound imaging. This framework incorporates gestational age filtering, precise identification of fetal standard planes, and targeted segmentation of brain regions to enhance diagnostic accuracy. Furthermore, we introduce the use of denoising diffusion probabilistic models in this context, marking a significant innovation in detecting previously unrecognized anomalies. We rigorously evaluated the framework using various diffusion-based anomaly detection methods, noise types, and noise levels. Notably, AutoDDPM emerged as the most effective, achieving an area under the precision-recall curve of 79.8% in detecting anomalies. This advancement holds promise for improving the tools available for nuanced and effective prenatal diagnostics.
KW - Fetal Ultrasound Screening
KW - Medical Imaging
UR - http://www.scopus.com/inward/record.url?scp=85206468612&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-73647-6_21
DO - 10.1007/978-3-031-73647-6_21
M3 - Conference contribution
AN - SCOPUS:85206468612
SN - 9783031736469
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 220
EP - 230
BT - Simplifying Medical Ultrasound - 5th International Workshop, ASMUS 2024, Held in Conjunction with MICCAI 2024, Proceedings
A2 - Gomez, Alberto
A2 - Khanal, Bishesh
A2 - King, Andrew
A2 - Namburete, Ana
PB - Springer Science and Business Media Deutschland GmbH
T2 - 5th International Workshop on Advances in Simplifying Medical Ultrasound, ASMUS 2024, held in conjunction with the 27th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024
Y2 - 6 October 2024 through 6 October 2024
ER -