Academic radiology · 2025
A Preliminary Study on an Intelligent Segmentation and Classification Model for Amygdala-Hippocampus MRI Images in Alzheimer's Disease.
СтатьяМногоцентровое исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Liu Siyu, Zhou Kun, Geng Daoying
Аннотация
BACKGROUND: This study developed a deep learning model for segmenting and classifying the amygdala-hippocampus in Alzheimer's disease (AD), using a large-scale neuroimaging dataset to improve early AD detection and intervention.
METHODS: We collected 1000 healthy controls (HC) and 1000 AD patients as internal training data from 15 Chinese medical centers. The independent external validation dataset was sourced from another three centers. All subjects underwent neuroimaging and neuropsychological assessments. A semi-automated annotation pipeline was used: the amygdala-hippocampus of 200 cases in each group were manually annotated to train the U²-Net segmentation model, followed by model annotation of 800 cases with iterative refinement. The DenseNet-121 architecture was built for automated classification. The robustness of the model was evaluated using an external validation set.
RESULTS: All 18 medical centers were distributed across diverse geographical regions in China. AD patients had lower MMSE/MoCA scores. Amygdala and hippocampal volumes were smaller in AD. Semi-automated annotation improved segmentation with DSC all exceeding 0.88 (P＜0.001). The final DSC of the 2000-case cohort was 0.914 in the training set and 0.896 in the testing set. The classification model achieved an AUC of 0.905. The external validation set comprised 100 cases in each group, and it can achieve an AUC of 0.835.
CONCLUSION: The amygdala-hippocampus recognition precision may be improved by the deep learning-based semi-automated approach and classification model, which will help with AD evaluation, diagnosis, and clinical AI application.
METHODS: We collected 1000 healthy controls (HC) and 1000 AD patients as internal training data from 15 Chinese medical centers. The independent external validation dataset was sourced from another three centers. All subjects underwent neuroimaging and neuropsychological assessments. A semi-automated annotation pipeline was used: the amygdala-hippocampus of 200 cases in each group were manually annotated to train the U²-Net segmentation model, followed by model annotation of 800 cases with iterative refinement. The DenseNet-121 architecture was built for automated classification. The robustness of the model was evaluated using an external validation set.
RESULTS: All 18 medical centers were distributed across diverse geographical regions in China. AD patients had lower MMSE/MoCA scores. Amygdala and hippocampal volumes were smaller in AD. Semi-automated annotation improved segmentation with DSC all exceeding 0.88 (P＜0.001). The final DSC of the 2000-case cohort was 0.914 in the training set and 0.896 in the testing set. The classification model achieved an AUC of 0.905. The external validation set comprised 100 cases in each group, and it can achieve an AUC of 0.835.
CONCLUSION: The amygdala-hippocampus recognition precision may be improved by the deep learning-based semi-automated approach and classification model, which will help with AD evaluation, diagnosis, and clinical AI application.