Supplementary Material for: Automatic Segmentation of Parkinson Disease Therapeutic Targets Using Nonlinear Registration and Clinical MR Imaging: Comparison of Methodology, Presence of Disease, and Quality Control

Introduction: Accurate and precise delineation of the globus pallidus pars interna (GPi) and subthalamic nucleus (STN) is critical for the clinical treatment and research of Parkinson’s disease (PD). Automated segmentation is a developing technology which addresses limitations of visualizing deep nuclei on MR imaging and standardizing their definition in research applications. We sought to compare manual segmentation with three workflows for template-to-patient nonlinear registration providing atlas-based automatic segmentation of deep nuclei. Methods: Bilateral GPi, STN, and red nucleus (RN) were segmented for 20 PD and 20 healthy control (HC) subjects using 3T MRIs acquired for clinical purposes. The automated workflows used were an option available in clinical practice and two common research protocols. Quality control (QC) was performed on registered templates via visual inspection of readily discernible brain structures. Manual segmentation using T1, proton density, and T2 sequences was used as “ground truth” data for comparison. Dice similarity coefficient (DSC) was used to assess agreement between segmented nuclei. Further analysis was done to compare the influences of disease state and QC classifications on DSC. Results: Automated segmentation workflows (CIT-S, CRV-AB, and DIST-S) had the highest DSC for the RN and lowest for the STN. Manual segmentations outperformed automated segmentation for all workflows and nuclei; however, for 3/9 workflows (CIT-S STN, CRV-AB STN, and CRV-AB GPi) the differences were not statically significant. HC and PD only showed significant differences in 1/9 comparisons (DIST-S GPi). QC classification only demonstrated significantly higher DSC in 2/9 comparisons (CRV-AB RN and GPi). Conclusion: Manual segmentations generally performed better than automated segmentations. Disease state does not appear to have a significant effect on the quality of automated segmentations via nonlinear template-to-patient registration. Notably, visual inspection of template registration is a poor indicator of the accuracy of deep nuclei segmentation. As automatic segmentation methods continue to evolve, efficient and reliable QC methods will be necessary to support safe and effective integration into clinical workflows.引言：精准勾勒内苍白球（globus pallidus pars interna, GPi）与丘脑底核（subthalamic nucleus, STN）的边界，对帕金森病（Parkinson’s disease, PD）的临床治疗与研究至关重要。自动化分割技术正逐步发展，可解决磁共振成像（MR imaging）中深部核团可视化不足的问题，并统一其在研究中的定义标准。本研究旨在对比手动分割与三种基于模板-患者非线性配准的工作流，以实现基于图谱的深部核团自动分割。 方法：本研究纳入20名帕金森病患者与20名健康对照（healthy control, HC）受试者，采用临床采集的3T磁共振影像，对双侧内苍白球、丘脑底核与红核（red nucleus, RN）进行分割。所用自动化工作流包含一项临床可用方案与两种通用研究方案。通过对易于辨识的脑部结构进行目视检查，对配准后的模板开展质量控制（quality control, QC）。以基于T1、质子密度与T2序列的手动分割作为对比的"金标准"数据。采用戴斯相似系数（Dice similarity coefficient, DSC）评估分割核团间的一致性。进一步分析疾病状态与质量控制分类对戴斯相似系数的影响。 结果：自动化分割工作流（CIT-S、CRV-AB与DIST-S）对红核的戴斯相似系数最高，对丘脑底核则最低。所有工作流与核团的手动分割效果均优于自动分割，但其中3项（CIT-S丘脑底核、CRV-AB丘脑底核与CRV-AB内苍白球）的差异无统计学意义。健康对照与帕金森病患者仅在1项对比中存在显著差异（DIST-S内苍白球）。质量控制分类仅在2项对比中体现出显著更高的戴斯相似系数（CRV-AB红核与内苍白球）。 结论：整体而言，手动分割的效果优于自动分割。疾病状态似乎对基于非线性模板-患者配准的自动分割质量无显著影响。值得注意的是，对模板配准的目视检查无法有效反映深部核团分割的准确性。随着自动分割技术的不断发展，高效可靠的质量控制方法将成为保障其安全、有效地整合入临床工作流的必要条件。