基于深度学习的帕金森病多巴胺转运体 SPECT 跨模态纹状体分割技术

IF 4.6 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-03-08 DOI:10.1109/TRPMS.2024.3398360
Haiyan Wang;Han Jiang;Gefei Chen;Yu Du;Zhonglin Lu;Zhanli Hu;Greta S. P. Mok
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引用次数: 0

摘要

多巴胺转运体(DaT)SPECT 的纹状体分割对于量化帕金森病(PD)的纹状体摄取量非常必要,但由于分辨率较低,因此具有挑战性。这项研究提出了一种跨模态纹状体自动分割方法,利用深度学习(DL)方法从临床 SPECT 图像中估计 MR 导出的纹状体轮廓。该研究分析了帕金森病进展标志物倡议数据库中的 123I-Ioflupane DaT SPECT 和 T1 加权 MR 图像,这些图像来自 200 名患有帕金森病的 152 名受试者和 48 名健康对照者。对 SPECT 和 MR 图像进行注册,并从 MR 图像中手动分割出四个纹状体区段轮廓作为标签。比较采用的 DL 方法包括 nnU-Net、U-Net、生成式对抗网络和基于 SPECT 阈值的方法。SPECT 和 MR 标签对被分成训练组、验证组和测试组 (136:24:40)。对骰子、豪斯多夫距离(HD)95%、相对体积差(RVD)、纹状体结合率(SBR)和不对称指数(ASI)进行分析。结果表明,与其他方法相比,nnU-Net 在所有纹状体区和整个纹状体的 Dice(约 0.7)、HD 95%(约 1.8)和 RVD(约 0.1)方面都有更好的表现。在临床帕金森病评估中,nnU-Net 还具有很强的 SBR 一致性(平均差为 -0.012)和 ASI 相关性(皮尔逊相关系数为 0.81)。所提出的基于DL的跨模态纹状体分割方法在临床DaT SPECT治疗帕金森病中是可行的。
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Deep-Learning-Based Cross-Modality Striatum Segmentation for Dopamine Transporter SPECT in Parkinson’s Disease
Striatum segmentation on dopamine transporter (DaT) SPECT is necessary to quantify striatal uptake for Parkinson’s disease (PD), but is challenging due to the inferior resolution. This work proposes a cross-modality automatic striatum segmentation, estimating MR-derived striatal contours from clinical SPECT images using the deep learning (DL) methods. 123 I-Ioflupane DaT SPECT and T1-weighted MR images from 200 subjects with 152 PD and 48 healthy controls are analyzed from the Parkinson’s progression markers initiative database. SPECT and MR images are registered, and four striatal compartment contours are manually segmented from MR images as the label. DL methods including nnU-Net, U-Net, generative adversarial networks, and SPECT thresholding-based method are implemented for comparison. SPECT and MR label pairs are split into train, validation, and test groups (136:24:40). Dice, Hausdorff distance (HD) 95%, and relative volume difference (RVD), striatal binding ratio (SBR) and asymmetry index (ASI) are analyzed. Results show that nnU-Net achieves better Dice (~0.7), HD 95% (~1.8), and RVD (~0.1) as compared to other methods for all striatal compartments and whole striatum. For clinical PD evaluation, nnU-Net also yields strong SBR consistency (mean difference, −0.012) and ASI correlation (Pearson correlation coefficient, 0.81). The proposed DL-based cross-modality striatum segmentation method is feasible for clinical DaT SPECT in PD.
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来源期刊
IEEE Transactions on Radiation and Plasma Medical Sciences
IEEE Transactions on Radiation and Plasma Medical Sciences RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
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8.00
自引率
18.20%
发文量
109
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