Deep-Learning-Based Cross-Modality Striatum Segmentation for Dopamine Transporter SPECT in Parkinson’s Disease

Abstract

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.