用常规T1 MRI实现深部灰质核团精准分割，支持大规模数据库研究。
Comprehensive segmentation of deep grey nuclei from structural MRI data
- 通过HIPS合成白质抑制图像，结合多图谱融合提升分割精度。
- 在1.5/3/7T下均表现稳定，所有结构Dice系数均超0.7。
- 适合需利用公共T1数据开展深部核团研究的学者使用。
动机：缺乏一种可复现、可重复的单一软件工具，对深部灰质核团进行完整分割。目标：提出一种快速、准确、鲁棒的方法，基于常规场强下的结构T1 MRI数据，分割丘脑核团、基底节、外侧裂皮层及红核。方法：利用近期提出的基于直方图多项式合成（HIPS）技术，从标准T1图像合成白质抑制（WMn）类似图像，并采用多图谱联合标签融合方法进行分割。结果：该方法在1.5/3/7T场强下均表现稳健，与人工标注对比，所有结构的骰子系数（Dice coefficient）均达到0.7或更高。影响：该方法使研究人员能够利用大型公开数据库中的常规T1数据，深入探究深部灰质核团的作用，此前因缺乏可靠可复现的分割工具而难以实现。
原文摘要 · Abstract (English)
Motivation: Lack of tools for comprehensive and complete segmentation of deep grey nuclei using a single software for reproducibility and repeatability Goal(s): A fast accurate and robust method for segmentation of deep grey nuclei (thalamic nuclei, basal ganglia, claustrum, red nucleus) from structural T1 MRI data at conventional field strengths Approach: We leverage the improved contrast of white-matter-nulled imaging by using the recently proposed Histogram-based Polynomial Synthesis (HIPS) to synthesize WMn-like images from standard T1 and then use a multi-atlas segmentation with joint label fusion to segment deep grey nuclei. Results: The method worked robustly on all field strengths (1.5/3/7) and Dice coefficients of 0.7 or more were achieved for all structures compared against manual segmentation ground truth. Impact: This method facilitates careful investigation of the role of deep grey nuclei by enabling the use of conventional T1 data from large public databases, which has not been possible, hitherto, due to lack of robust reproducible segmentation tools.
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