Evaluation of Uncertainty-Aware Multi-Software Ensembles for Hippocampal Segmentation
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...
| Authors | Gabriel Oliveira-Stahl, Anna Schroder, James Moggridge et al. |
|---|---|
| Primary category | cs.CV |
| Published | 22 October 2026 |
| Last updated at source | 22 October 2026 |
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