Evaluation on Auto-segmentation of the Clinical Target Volume (CTV) for Graves' Ophthalmopathy (GO) with a Fully Convolutional Network (FCN) on CT Images

Purpose: The aim of this study is to evaluate the accuracy and dosimetric effects for auto-segmentation of the CTV for GO in CT images based on FCN. Methods: An FCN-8s network architecture for auto-segmentation was built based on Caffe. CT images of 121 patients with GO who have received radiotherapy at the West China Hospital of Sichuan University were randomly selected for training and testing. Two methods were used to segment the CTV of GO: treating the two-part CTV as a whole anatomical region or considering the two parts of CTV as two independent regions. Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD) were used as evaluation criteria. The auto-segmented contours were imported into the original treatment plan to analyse the dosimetric characteristics. Results: The similarity comparison between manual contours and auto-segmental contours showed an average DSC value of up to 0.83. The max HD values for segmenting two parts of CTV separately was a little bit smaller than treating CTV with one label (8.23 +/- 2.80 vs. 9.03 +/- 2.78). The dosimetric comparison between manual contours and auto-segmental contours showed there was a significant difference (p<0.05) with the lack of dose for auto-segmental CTV. Conclusion: Based on deep learning architecture, the automatic segmentation model for small target areas can carry out auto contouring tasks well. Treating separate parts of one target as different anatomic regions can help to improve the auto-contouring quality. The dosimetric evaluation can provide us with different perspectives for further exploration of automatic sketching tools.