Three-dimensional deep neural network for automatic delineation of cervical cancer in planning computed tomography images

Abstract

Purpose: Radiation therapy is an essential treatment modality for cervical cancer, while accurate and efficient segmentation methods are needed to improve the workflow. In this study, a three-dimensional V-net model is proposed to automatically segment clinical target volume (CTV) and organs at risk (OARs), and to provide prospective guidance for low lose area. Material and methods: A total of 130 CT datasets were included. Ninety cases were randomly selected as the training data, with 10 cases used as the validation data, and the remaining 30 cases as testing data. The V-net model was implemented with Tensorflow package to segment the CTV and OARs, as well as regions of 5 Gy, 10 Gy, 15 Gy, and 20 Gy isodose lines covered. The auto-segmentation by V-net was compared to auto-segmentation by U-net. Four representative parameters were calculated to evaluate the accuracy of the delineation, including Dice similarity coefficients (DSC), Jaccard index (JI), average surface distance (ASD), and Hausdorff distance (HD). Results: The V-net and U-net achieved the average DSC value for CTV of 0.85 and 0.83, average JI values of 0.77 and 0.75, average ASD values of 2.58 and 2.26, average HD of 11.2 and 10.08, respectively. As for the OARs, the performance of the V-net model in the colon was significantly better than the U-net model (p = 0.046), and the performance in the kidney, bladder, femoral head, and pelvic bones were comparable to the U-net model. For prediction of low-dose areas, the average DSC of the patients’ 5 Gy dose area in the test set were 0.88 and 0.83, for V-net and U-net, respectively. Conclusions: It is feasible to use the V-Net model to automatically segment cervical cancer CTV and OARs to achieve a more efficient radiotherapy workflow. In the delineation of most target areas and OARs, the performance of V-net is better than U-net. It also offers advantages with its feature of predicting the low-dose area prospectively before radiation therapy (RT).