用多模态MRI提升丘脑核团分割精度，准确率超现有方法。
RATNUS: Rapid, Automatic Thalamic Nuclei Segmentation using Multimodal MRI inputs
- 融合多时相合成T1与扩散特征增强丘脑核团对比度。
- 平均真阳性率87.19%，显著高于FreeSurfer和THOMAS。
- 适合神经影像研究与脑疾病精准诊断人群使用。
丘脑核团的精确分割对理解脑功能和改善疾病治疗至关重要。传统方法通常仅依赖单一T1加权图像，其在丘脑区域对比度有限。本文提出RATNUS，利用多反转时间生成的合成T1加权图像结合扩散衍生特征，提升丘脑内核团的可视性。基于这些特征，采用卷积神经网络实现13个丘脑核团的分割。为便于比较，我们引入统一的核团标注方案。结果表明，RATNUS相对于人工标注的平均真阳性率为87.19%；而FreeSurfer和THOMAS分别为64.25%和57.64%，验证了RATNUS在丘脑核团分割上的优越性。
原文摘要 · Abstract (English)
Accurate segmentation of thalamic nuclei is important for better understanding brain function and improving disease treatment. Traditional segmentation methods often rely on a single T1-weighted image, which has limited contrast in the thalamus. In this work, we introduce RATNUS, which uses synthetic T1-weighted images with many inversion times along with diffusion-derived features to enhance the visibility of nuclei within the thalamus. Using these features, a convolutional neural network is used to segment 13 thalamic nuclei. For comparison with other methods, we introduce a unified nuclei labeling scheme. Our results demonstrate an 87.19% average true positive rate (TPR) against manual labeling. In comparison, FreeSurfer and THOMAS achieve TPRs of 64.25% and 57.64%, respectively, demonstrating the superiority of RATNUS in thalamic nuclei segmentation.
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