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Mykula, H.* ; Gasser, L.* ; Lobmaier, S.* ; Schnabel, J.A. ; Zimmer, V.* ; Bercea, C.-I.

Diffusion models for unsupervised anomaly detection in fetal brain ultrasound.

In: (Simplifying Medical Ultrasound). Berlin [u.a.]: Springer, 2025. 220-230 (Lect. Notes Comput. Sc. ; 15186 LNCS)
DOI
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.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
Schlagwörter Fetal Ultrasound Screening ; Medical Imaging
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel Simplifying Medical Ultrasound
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 15186 LNCS, Heft: , Seiten: 220-230 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Institute for Machine Learning in Biomed Imaging (IML)