Geometric and Dosimetric Evaluation of the Automatic Delineation of Organs at Risk (OARs) in Non-Small-Cell Lung Cancer Radiotherapy Based on a Modified DenseNet Deep Learning Network

Fuli Zhang; Qiusheng Wang; Anning Yang; Na Lu; Huayong Jiang; Diandian Chen; Yanjun Yu; Yadi Wang

doi:10.3389/fonc.2022.861857

Geometric and Dosimetric Evaluation of the Automatic Delineation of Organs at Risk (OARs) in Non-Small-Cell Lung Cancer Radiotherapy Based on a Modified DenseNet Deep Learning Network

Front Oncol. 2022 Mar 15:12:861857. doi: 10.3389/fonc.2022.861857. eCollection 2022.

Authors

Fuli Zhang¹, Qiusheng Wang², Anning Yang², Na Lu¹, Huayong Jiang¹, Diandian Chen¹, Yanjun Yu¹, Yadi Wang¹

Affiliations

¹ Radiation Oncology Department, The Seventh Medical Center of Chinese People's Liberation Army (PLA) General Hospital, Beijing, China.
² School of Automation Science and Electrical Engineering, Beihang University, Beijing, China.

Abstract

Purpose: To introduce an end-to-end automatic segmentation model for organs at risk (OARs) in thoracic CT images based on modified DenseNet, and reduce the workload of radiation oncologists.

Materials and methods: The computed tomography (CT) images of 36 lung cancer patients were included in this study, of which 27 patients' images were randomly selected as the training set, 9 patients' as the testing set. The validation set was generated by cross validation and 6 patients' images were randomly selected from the training set during each epoch as the validation set. The autosegmentation task of the left and right lungs, spinal cord, heart, trachea and esophagus was implemented, and the whole training time was approximately 5 hours. Geometric evaluation metrics including the Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95) and average surface distance (ASD), were used to assess the autosegmentation performance of OARs based on the proposed model and were compared with those based on U-Net as benchmarks. Then, two sets of treatment plans were optimized based on the manually contoured targets and OARs (Plan1), as well as the manually contours targets and the automatically contoured OARs (Plan2). Dosimetric parameters, including Dmax, Dmean and Vx, of OARs were obtained and compared.

Results: The DSC, HD95 and ASD of the proposed model were better than those of U-Net. The differences in the DSC of the spinal cord and esophagus, differences in the HD95 of the spinal cord, heart, trachea and esophagus, as well as differences in the ASD of the spinal cord were statistically significant between the two models (P<0.05). The differences in the dose-volume parameters of the two sets of plans were not statistically significant (P>0.05). Moreover, compared with manual segmentation, autosegmentation significantly reduced the contouring time by nearly 40.7% (P<0.05).

Conclusions: The bilateral lungs, spinal cord, heart and trachea could be accurately delineated using the proposed model in this study; however, the automatic segmentation effect of the esophagus must still be further improved. The concept of feature map reuse provides a new idea for automatic medical image segmentation.

Keywords: DenseNet; deep learning; feature reuse; medical image segmentation; non-small-cell lung cancer; organs at risk.