新生兒腦部結構的形態測量分析對於認知與表達能力的發育中作為重要的評估指標，在臨床研究中，依據解剖特徵將大腦進行細緻、明確的分類分區，用以評估不同區域中功能性的意義與影響。為了達到精細的分割結果，現有研究團隊主要針對新生兒腦部核磁共振成像（Magnetic Resonance Imaging, MRI）影像為主的基於圖譜分割方法（例如：dHCP），在於面對不同醫院收錄的 MRI，容易受到掃描設定、出生週數和影像品質等差異造成分割誤差，並且一套完整的分割流程將會花費大量時間，即使在前期分割過程中產生了分割錯誤，也無法中止後續的皮質灰質表面重建流程。除此之外，現有的深度學習方法，雖然通過神經網路減少了運算與時間成本，卻只能分割粗略的腦區，影響後續發育評估分析，而也未有分割模型應用於跨數據集（如：早產與足月嬰兒）。因此本研究中，我們提出了一種基於深度學習的分層式細節網路（Hierarchical Detail Network, HiDeNet）來解決基於圖譜分割方法的缺陷，同時多腦區分割的目標。首先，選用高品質的足月兒 MRI 影像作為訓練數據，而將低品質、低解析度的早產兒 MRI 影像用於推理。為了提高模型對早產兒資料集的應用性，我們在訓練過程中對高品質影像進行隨機數據增強，模擬掃描過程中可能產生的問題（包括假影與晃動），以增強模型對低品質影像的適應能力。接著，藉由 U-Net 的骨幹網路達到分層式分割，第一個網路透過 MRI 影像學習腦區組織資訊有效地用於後續輸入特徵，第二個網路則是分割出 87 個腦區結構。推理部分通過過客制化處理去除多餘骨骼與影像強度歸一化，減少測試資料的品質問題，並透過補丁式（Patch-based）推理完成每個案例的分割。為了證明 HiDeNet 的性能，使用了兩種不同的數據集與多個先進的深度學習方法進行相比，結果表明，與現有方法相比，我們的方法實現了更高的分割準確率與穩定性，且表示 HiDeNet 分割的腦區結果能應用於實際分析算法中。

Neonatal brain morphometrics are crucial for evaluating cognitive and expressive development. Current atlas-based methods, such as Developing Human Connectome Project (dHCP) structural pipeline, for brain magnetic resonance imaging (MRI) segmentation often face challenges due to variable scan settings and quality, leading to errors and a time-consuming process. Existing deep learning approaches, while faster, often fail to provide detailed segmentation necessary for developmental assessments and typically don't cater to diverse datasets, e.g., preterm and full-term infants. In response, this study introduces the hierarchical detail network (HiDeNet), a deep learning-based solution that overcomes these limitations by providing layered, detailed segmentation across different datasets. HiDeNet trains on high-quality full-term MRI images and infers on lower quality, preterm images, incorporating random data augmentation to simulate and adapt to scanning issues. It employs a U-Net backbone to learn from broad to fine brain region features, ensuring accurate segmentation of 87 brain structures. Customized preprocessing modules and patch-based inference further enhance its adaptability and accuracy across datasets. Our comparisons with other advanced methods using diverse datasets demonstrate HiDeNet's superior segmentation accuracy and stability, making it a robust tool for neonatal brain analysis and further research.

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