Gabriel Oliveira-Stahl, Anna Schroder, James C. Moggridge, Hamza A. Salhab, Caroline Micallef, Josephine Barnes, M. Jorge Cardoso, Carole H. Sudre, Matthew Grech‐Sollars · 2026 · The Journal of Machine Learning for Biomedical Imaging
DOI: 10.59275/j.melba.2026-e77b · cited by 0 works (OpenAlex) · open-access PDF
La segmentación de conjunto hipocampal precisa puede ser una herramienta útil para diagnosticar y monitorear condiciones neurológicas severas como la enfermedad de Alzheimer y la epilepsia. Mientras existen numerosos métodos de segmentación automatizada, su adopción clínica sigue siendo limitada. La evaluación de incertidumbre confiable puede aumentar la confianza y facilitar la traducción clínica. Este estudio evalúa conjuntos de cinco métodos heterogéneos de segmentación hipocampal - InnerEye, ASHS, FastSurfer, HippoSeg y FreeSurfer - en dos conjuntos de datos de demencia y un conjunto de datos de epilepsia. El subconjunto que contiene el estudio InnerEye, FastSurfer y HippoSeg apareció como exactos y eficientes, destacando la viabilidad de equilibrar
Machine translation (Meta M2M100); the English original follows.
Accurate hippocampal segmentation can be a useful tool for diagnosing and monitoring neurological conditions such as Alzheimer’s disease and epilepsy. While numerous automated segmentation methods exist, their clinical adoption remains limited. Reliable uncertainty assessment can enhance trust and facilitate clinical translation. This study evaluates ensembles of five heterogeneous hippocampal segmentation methods — InnerEye, ASHS, FastSurfer, HippoSeg, and FreeSurfer — across two dementia datasets and one epilepsy dataset. The sub-ensemble containing InnerEye, FastSurfer, and HippoSeg emerged as both accurate and efficient, highlighting the feasibility of balancing computational cost and performance. Additionally, ensemble-derived uncertainty quantification with sample variance, mutual information, and predictive entropy is shown to reduce inaccurate segmentations by flagging low-confidence cases, potentially providing a mechanism for automatically escalating ambiguous cases for expert assessment. Lastly, this study explores the downstream clinical utility of ensemble-derived uncertainty, finding a significant correlation with motion artefact severity and a modest contribution to Alzheimer’s disease classification.
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Topics: Segmentation, Flagging, Computer science, Artificial intelligence, Entropy (arrow of time), Machine learning
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