ACTIVITY CONCENTRATION ESTIMATION IN AUTOMATED KIDNEY SEGMENTATION BASED ON CONVOLUTION NEURAL NETWORK METHOD FOR 177LU-SPECT/CT KIDNEY DOSIMETRY

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.