For 177Lu-DOTATATE treatments, dosimetry based on manual kidney segmentation from computed tomography (CT) is accurate but time consuming and might be affected by misregistration between CT and SPECT images. This study develops a convolution neural network (CNN) for automated kidney segmentation that accurately aligns CT segmented volume of interest (VOI) to the kidneys in SPECT images. The CNN was trained with SPECT/CT images performed over the abdominal area of 137 patients treated with 177Lu-DOTATATE. Activity concentrations in automated and manual segmentations were strongly correlated for both kidneys (r > 0.96, p < 0.01) in the testing cohort (n = 20). The Bland–Altman analyses demonstrated higher accuracy for the CNN segmentation compared to the manual segmented kidneys without VOI adjustment. The CNN demonstrated a potential for accurate kidney segmentation. The CNN was a fast and robust approach for assessment of activity concentrations in SPECT images, and performed equally well as the manual segmentation method.

中文翻译：

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基于卷积神经网络方法的 177LU–SPECT/CT 肾脏剂量学自动肾脏分割中的活动浓度估计

对于 177Lu-DOTATATE 治疗，基于计算机断层扫描 (CT) 手动肾脏分割的剂量测定准确但耗时，并且可能受到 CT 和 SPECT 图像之间配准错误的影响。本研究开发了一种用于自动肾脏分割的卷积神经网络 (CNN)，可准确地将 CT 分割的感兴趣体积 (VOI) 与 SPECT 图像中的肾脏对齐。CNN 使用 SPECT/CT 图像对 137 名接受 177Lu-DOTATATE 治疗的患者的腹部区域进行训练。在测试队列 (n = 20) 中，自动和手动分割中的活动浓度与两个肾脏密切相关 (r > 0.96, p < 0.01)。Bland-Altman 分析表明，与没有 VOI 调整的手动分割肾脏相比，CNN 分割的准确性更高。CNN 展示了精确肾脏分割的潜力。CNN 是一种快速且稳健的方法，用于评估 SPECT 图像中的活动浓度，并且性能与手动分割方法一样好。