L-FUSION: Laplacian Fetal Ultrasound Segmentation and Uncertainty Estimation

Müller J, Wright R, Day TG, Venturini L, Budd SF, Reynaud H, Hajnal JV, Razavi R, Kainz B (2026)

Publication Type: Conference contribution

Publication year: 2026

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 16165 LNCS

Pages Range: 164-173

Conference Proceedings Title: Lecture Notes in Computer Science

Event location: Daejeon KR

ISBN: 9783032063281

DOI: 10.1007/978-3-032-06329-8_16

Abstract

Accurate analysis of prenatal ultrasound (US) is essential for early detection of developmental anomalies. However, operator dependency and technical limitations (e.g. intrinsic artefacts and effects, setting errors) can complicate image interpretation and the assessment of diagnostic uncertainty. We present L-FUSION (Laplacian Fetal US Segmentation with Integrated FoundatiON models), a framework that integrates uncertainty quantification through unsupervised, normative learning and large-scale foundation models for robust segmentation of fetal structures in normal and pathological scans. We propose to utilise the aleatoric logit distributions of Stochastic Segmentation Networks and Laplace approximations with fast Hessian estimations to estimate epistemic uncertainty only from the segmentation head. This enables us to achieve reliable abnormality quantification for instant diagnostic feedback. Combined with an integrated Dropout component, L-FUSION enables reliable differentiation of lesions from normal fetal anatomy with enhanced uncertainty maps and segmentation counterfactuals in US imaging. It improves epistemic and aleatoric uncertainty interpretation and removes the need for manual disease-labelling. Evaluations across multiple datasets show that L-FUSION achieves superior segmentation accuracy and consistent uncertainty quantification, supporting on-site decision-making and offering a scalable solution for advancing fetal ultrasound analysis in clinical settings. Code is available at https://github.com/ividja/L-FUSION.

Authors with CRIS profile

Johanna Müller Professur für Image Data Exploration and Analysis Hadrien Jean-Francois Reynaud
Bernhard Kainz Professur für Image Data Exploration and Analysis

Involved external institutions

King’s College London GB United Kingdom (GB) Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB)

How to cite

APA:

Müller, J., Wright, R., Day, T.G., Venturini, L., Budd, S.F., Reynaud, H.,... Kainz, B. (2026). L-FUSION: Laplacian Fetal Ultrasound Segmentation and Uncertainty Estimation. In Dong Ni, Ruobing Huang, Wufeng Xue, Alison Noble (Eds.), Lecture Notes in Computer Science (pp. 164-173). Daejeon, KR: Springer Science and Business Media Deutschland GmbH.

MLA:

Müller, Johanna, et al. "L-FUSION: Laplacian Fetal Ultrasound Segmentation and Uncertainty Estimation." Proceedings of the 6th International Workshop on Advances in Simplifying Medical Ultrasound, ASMUS 2025, Held in Conjunction with the Medical Image Computing and Computer-Assisted Intervention, MICCAI 2025, Daejeon Ed. Dong Ni, Ruobing Huang, Wufeng Xue, Alison Noble, Springer Science and Business Media Deutschland GmbH, 2026. 164-173.

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