Automatic Delineation of the Clinical Target Volume in Rectal Cancer for Radiation Therapy using Three-dimensional Fully Convolutional Neural Networks

Accurate, robust, and fast delineation of the clinical target volume (CTV) for the use in radiotherapy of rectal cancer (RC) is highly sought-after. Convolutional neural networks (CNNs) have proven themselves very effective in various segmentation tasks on medical images. Despite this, their application in CTV delineation is not yet fully explored. This study uses the three-dimensional fully convolutional neural network architecture called V-net for CTV delineation. The West China Hospital (Chengdu, China) provided this study with 120 annotated CT scans. For improved performance and to battle data scarcity, the available scans were augmented. Trained on 100 CT-scans for 20 hours and tested on 20 previously unseen CT-scans the network achieved a mean dice similarity coefficient (DSC) of 0.90 and a mean delineation time per CTV of 0.60 seconds. The proposed method is compared with two other state-of-the-art CNNs and is shown to be superior.