Region proposal network for multi-organ segmentation in CT for pancreatic radiotherapy

Abstract

Delineation of organs-at-risk (OARs) is a labor-intensive and time-consuming procedure in radiation treatment planning process. This work aims to develop a deep learning-based method for rapid and accurate pancreatic multi-organ delineation with the goal of expediting the treatment planning process. A retrospective investigation has been carried out on twenty patients with contours delineated on CT simulation scan. Eight OARs including large bowel, small bowel, duodenum, left kidney, right kidney, liver, spinal cord and stomach were selected for automated segmentation. In order to achieve expert level of accuracy, we proposed a regional proposal network which consists of two stages of feature extraction from given CT images. A coarse feature map was first obtained using a backbone network, and then the refined feature map was extracted using mask regional convolutional neural network. Metrics including Dice similarity coefficient (DSC), sensitivity, specificity, Hausdorff distance 95% (HD95), mean surface distance (MSD) and residual mean square distance (RMSD) were computed to quantitatively assess the performance of our proposed method. DSC values of 0.90±0.09, 0.87±0.13, 0.82±0.12, 0.95±0.03, 0.95±0.06, 0.96±0.02, 0.90±0.03, and 0.94±0.02 were achieved for large bowel, small bowel, duodenum, left kidney, right kidney, liver, spinal cord and stomach, respectively.