Artificial Intelligence-assisted Automatic Delineation Of The Hippocampus, An Organ At Risk In Brain Radiotherapy

Background: Whole brain radiotherapy(WBRT)can impair patients' cognitive function.Hippocampal avoidance during WBRT can potentially prevent this side effect.However,manually delineating the target area is time-consuming and difficult.Here,we proposed a credible approach of automatic hippocampal delineation based on convolutional neural networks.Methods: Referring to the hippocampus contouring atlas proposed by RTOG 0933,we manually delineated(MD)the hippocampus on the MRI data sets(3-dimensional T1-weighted with slice thickness of 1 mm,n=175),which were used to construct a threedimensional convolutional neural network aiming for the hippocampus automatic delineation(AD).The performance of this AD tool was tested on three cohorts:(a)3D T1 MRI with 1-mm slice thickness(n=30);(b)non-3D T1-weighted MRI with 3-mm slice thickness(n=19);(c)non-3D T1-weighted MRI with 1-mm slice thickness(n=11).Virtual radiation plans were created for AD and MD hippocampi in cohort c to evaluate the clinical feasibility of the artificial intelligence approach.Statistical analyses were performed using SPSS version 23.P < 0.05 was considered significant.Results: The Dice similarity coefficient(DSC)and Average Hausdorff Distance(AVD)between the AD and MD hippocampi are 0.86±0.028 and 0.18±0.050 cm in cohort a,0.76±0.035 and 0.31±0.064 cm in cohort b,0.80±0.015 and 0.24±0.021 cm in cohort c,respectively.The DSC and AVD in cohort a were better than those in cohorts b and c(P < 0.01).There is no significant difference between the radiotherapy plans generated using the AD and MD hippocampi.Conclusion: The AD of the hippocampus based on a deep learning algorithm showed satisfying results,which could have a positive impact on improving delineation accuracy and reducing work load.