Multi-organ Segmentation of Chest CT Images in Radiation Oncology: Comparison of Standard and Dilated UNet

Abstract

Automatic delineation of organs at risk (OAR) in computed tomography (CT) images is a crucial step for treatment planning in radiation oncology. However, manual delineation of organs is a challenging and time-consuming task subject to inter-observer variabilities. Automatic organ delineation has been relying on non-rigid registrations and atlases. However, lately deep learning appears as a strong competitor with specific architectures dedicated to image segmentation like UNet. In this paper, we first assessed the standard UNet to delineate multiple organs in CT images. Second, we observed the effect of dilated convolutional layers in UNet to better capture the global context from the CT images and effectively learn the anatomy, which results in increased localization of organ delineation. We evaluated the performance of a standard UNet and a dilated UNet (with dilated convolutional layers) on four chest organs (esophagus, left lung, right lung, and spinal cord) from 29 lung image acquisitions and observed that dilated UNet delineates the soft tissues notably esophagus and spinal cord with higher accuracy than the standard UNet. We quantified the segmentation accuracy of both models by computing spatial overlap measures like Dice similarity coefficient, recall & precision, and Hausdorff distance. Compared to the standard UNet, dilated UNet yields the best Dice scores for soft organs whereas for lungs, no significant difference in the Dice score was observed: 0.84 ± 0.07 vs 0.71 ± 0.10 for esophagus, 0.99 ± 0.01 vs 0.99 ± 0.01 for left lung, 0.99 ± 0.01 vs 0.99 ± 0.01 for right lung and 0.91 ± 0.05 vs 0.88 ± 0.04 for spinal cord.