Can Convolutional Neural Network Delineate OAR in Lung Cancer More Accurately and Efficiently Compared with Atlas Based Method？

Abstract

Delineation of organ at risk (OAR) is important but time-consuming for radiotherapy planning. Automatic segmentation of OAR based on a convolutional neural networks (CNN) with 101 layers has been established for lung cancer patients in our institution. The aim of this study was to compare the efficacy and accuracy of two automatic OAR contouring methods, CNN based segmentation and atlas-based contouring. The OARs, including the lungs, esophagus, heart and spinal cord, of ten NSCLC patients were delineated using three methods: (1) automatic segmentation based on CNN(AS-CNN); (2) automatic segmentation based on atlas by treatment planning software; (3) manually delineated(MD) by a senior radiation oncologist. The MD was used the ground truth for reference. The results of the automated contouring methods were compared by delineation time, Hausdorff Distance(HD), Mean Distance to Agreement(MDA), and Dice similarity coefficient (DSC).Paired t test was used to compare the accuracy between different methods. The manual delineation time was 21.7±2.9 min. Automatic segmentation time of AS-CNN and AS-Atlas was 1.6±0.2min(on a Titan XP graphics card) and 2.2±0.1min, which were only 7% and 10% of manual delineation time, respectively. AS-CNN was more efficiency than AS-Atlas(P<0.001). In terms of accuracy, the MDA and DSC were listed in table 1. The esophagus AS-CNN performed well, with a DSC of 0.77, while the AS-Atlas of esophagus was unavailable. AS-CNN showed marginal better than AS-Atlas in the segmentation of heart. The AS-CNN of spinal cord performed slightly worse the AS-Atlas. The results were similar in contouring of both lungs. AS-CNN significantly reduces contouring time compared with AS-Atlas. AS-CNN is viable and performed well, especially for heart and esophagus, while AS-Atlas performed better for the spinal cord. However, careful review and manually modifying is still required for both methods to assure the safety of radiotherapy. AS-CNN was in continuous evolution as more clinical data was collected in the database for AS-CNN, and it could provide more benefit.Abstract 3246; Table 1MDA and DSC of AS-CNN and AS-Atlas in automatic segmentationOrgansParameterAS-CNNAS-AtlasP valueLeft lungMDA(mm)1.14±0.152.08±2.170.207DSC0.95±0.010.92±0.050.076Right lungMDA(mm)2.87±3.092.78±3.270.404DSC0.94±0.020.94±0.020.892HeartMDA(mm)1.67±0.463.63±3.370.107DSC0.87±0.030.77±0.140.067Spinal cordMDA(mm)1.02±0.180.55±0.100.000DSC0.80±0.050.88±0.020.000 Open table in a new tab