Generalizability of AI-based image segmentation and centering estimation algorithm: a multi-region, multi-center, and multi-scanner study

We created and validated an open-access AI algorithm (AIc) for assessing image segmentation and patient centering in a multi-body-region, multi-center, and multi-scanner study. Our study included 825 head, chest, and abdomen-pelvis CT from 275 patients (153 females, 128 males; mean age 67 ± 14 years) scanned at five academic and community hospitals. CT images were processed with the AIc to determine vertical and horizontal centering at the skull base (head CT), carina (chest CT), and L2-L3 disc (abdomen CT). We manually measured the vertical and horizontal off-centering. We found strong correlations between AIc and manual estimate of off-centering in both the vertical (head, r = 0.93; chest, r = 0.94; abdomen, and r = 0.95) and horizontal directions (head CT, r = 0.85; chest, r = 0.85; abdomen, r = 0.8) and across age groups (r = 0.70-0.97), gender (r = 0.81-0.96), and multiple scanners from the five sites (r = 0.74-0.99). The AIc area under the receiver operating characteristic curve for centered and off-centered CT exams ranged from 0.72 (head) to 0.99 (chest). Therefore, our study showed that positron-emission tomography/CT (PET/CT) examinations commonly exhibit significant off-centering, particularly with vertical deviations often exceeding 30 mm and horizontal deviations between 10 and 30 mm. In addition, it demonstrated that our AI model can effectively assess both vertical and horizontal off-centering, although it performs better at estimating vertical off-centering.