Gabriel Oliveira-Stahl, Anna Schroder, James Moggridge, Hamza A. Salhab, Caroline Micallef, Josephine Barnes, M. Jorge Cardoso, Carole H. Sudre · 2026 · arXiv
DOI: 10.48550/arXiv.2610.31505 · open-access PDF
正確なヒッポカンパルセグメントは、アルツハイマー病やうつ病などの神経疾患の診断および監視のための有用なツールである可能性があります。多数の自動セグメント方法が存在する一方で、その臨床的採用は限られています。信頼性の高い不確実性評価は信頼性を高め、臨床翻訳を容易にすることができます。この研究は、InnerEye、FastSurfer、ASHS、FastSurfer、HippoSeg、FreeSurferの5つの異質なヒッポカンパルセグメント方法のセットを評価しています。InnerEye、FastSurfer、HippoSegを含むサブセットが出現しました。
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
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