Deep CNN-based Fully Automated Segmentation of Pelvic Multi-Organ on CT Images for Prostate Cancer Radiotherapy

Abstract

Background: Manual delineation of volumes for prostate radiotherapy treatment is a time-consuming task for radiation oncologists and is also prone to variability. Deep learning-based auto-segmentation methods showed promising results with accurate and high-fidelity contours. Objective: The objective of this study was to evaluate the feasibility of a Computed Tomography (CT)-based deep learning auto-segmentation algorithm for multi-organ delineation in prostate radiotherapy. Material and Methods: In this single-institution retrospective study, a total of 118 patients with prostate cancer were included. We applied 3D nnU-net deep convolu-tional neural network architecture, a self-adapting ensemble method for simultaneous fast and reproducible multi-organ auto-contouring. The dataset was randomly divided into training and test sets from 95 and 23 patients, respectively. Intensity-modulated radiotherapy plans were generated for both manual and automatic delineations using identical optimization settings. Contours were assessed in terms of the Dice Similarity Coefficient (DSC), and average Hausdorff Distance (HD). Dose distributions were additionally evaluated using parameters derived from Dose-Volume Histograms (DVH). Results: On the test set, 3D nnU-net achieved the best performance in the blad-der (DSC:0.97, HD:4.13), right femur head (DSC:0.96, HD:3.58), left femur head (DSC:0.96, HD:3.95), rectum (DSC:0.9, HD:10.04), prostate (DSC:0.82, HD:3.68), lymph nodes (DSC:0.77, HD:15.5), and seminal vesicles (DSC:0.69, HD:10.95). DVH parameters of targets and Organ at Risks (OARs) were significantly different except for lymph nodes and femoral heads between treatment plans based on manual and automatic contours. Conclusion: The 3D nnU-net architecture can be successfully used for multi-organ segmentation in the male pelvic area.