Diffusion Models for Unsupervised Anomaly Detection in Fetal Brain Ultrasound

Hanna Mykula, Lisa Gasser, Silvia Lobmaier, Julia A. Schnabel, Veronika Zimmer, Cosmin I. Bercea

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationSimplifying Medical Ultrasound - 5th International Workshop, ASMUS 2024, Held in Conjunction with MICCAI 2024, Proceedings
EditorsAlberto Gomez, Bishesh Khanal, Andrew King, Ana Namburete
PublisherSpringer Science and Business Media Deutschland GmbH
Pages220-230
Number of pages11
ISBN (Print)9783031736469
DOIs
StatePublished - 2025
Event5th 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 - Marrakesh, Morocco
Duration: 6 Oct 20246 Oct 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume15186 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th 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
Country/TerritoryMorocco
CityMarrakesh
Period6/10/246/10/24

Keywords

  • Fetal Ultrasound Screening
  • Medical Imaging

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