Development and Validation of a Deep Learning-Based Auto-Delineation of Target Volume and Organs at Risk in Pancreatic Cancer Radiotherapy.

Abstract

The delineation of the clinical target volume (CTV), gross target volume (GTV) and organs at risk (OARs) is a crucial and laborious in pancreatic cancer radiotherapy. In this work, we propose and evaluate a three-dimensional (3D) novel convolutional neural network (CNN) for automatic and accurate CTV, GTV and OARs in pancreatic cancer. A total of 120 computed tomography (CT) scans patients with pancreatic cancer were collected. A novel 3D CNN network, called ResUNet3D, was developed to achieve auto-delineation. 96 patients chosen randomly were used for training, 12 patients for validation, and 12 patients for testing. Meanwhile, the Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD95%) were used to assess the performance. The DSC values for the test data were 80.9±8.6%, 77.5±5.6%, 94.5±1.3%, 66.2±13.4%, 73.6±7.6%, 79.0±8.7%, 94.1±1.9%, 94.6±1.4%, 87.3±5.8% for CTV, GTV, liver, duodenum, spinal cord, bowel, kidney left, kidney right, stomach. The corresponding HD95% values were 10.7±6.9mm, 7.8±5.7mm, 11.6±5.6mm, 18.6±5.6mm, 2.7±0.7mm, 17.7±8.6mm, 3.9±1.4mm, 3.7±1.9mm, 13.4±5.7mm, respectively. The average delineation time for one patient's CT images was within 5 seconds. The experimental results demonstrate that the CTV, GTV and OARs delineated for pancreatic cancer by ResUNet3D achieved a close agreement with the ground truth. ResUNet3D could significantly reduce the radiation oncologists' contouring time.