| YANG Anning,LU Na,JIANG Huayong,CHEN Diandian,YU Yanjun,WANG Yadi,Wang Qiusheng,Zhang Fuli. Automatic delineation of organs at risk in non-small cell lung cancer radiotherapy based on deep learning networks. Oncol Transl Med, 2022, 8: 83-88. |
| 基于 DenseNet 深度学习网络的非小细胞肺癌放疗中危及器官（OARs）自动勾画的几何和剂量学评估 |
| Automatic delineation of organs at risk in non-small cell lung cancer radiotherapy based on deep learning networks |
| Received:January 15, 2022 Revised:April 19, 2022 |
| DOI：10.1007/s10330-022-0553-3 |
| 中文关键词: 危及器官；医学图像勾画；深度学习；DenseNet深度学习网络 |
| 英文关键词: non-small cell lung cancer; organs at risk; medical image segmentation; deep learning; DenseNet |
| 基金项目:首都临床特色专项课题（编号：Z181100001718011） |
| Author Name | Affiliation | Postcode | | YANG Anning | School of Automation Science and Electrical Engineering,Beihang University | 100191 | | LU Na | Radiation Oncology Department,the Seventh Medical Center of Chinese PLA General Hospital | | | JIANG Huayong | Radiation Oncology Department,the Seventh Medical Center of Chinese PLA General Hospital | | | CHEN Diandian | Radiation Oncology Department,the Seventh Medical Center of Chinese PLA General Hospital | | | YU Yanjun | Radiation Oncology Department,the Seventh Medical Center of Chinese PLA General Hospital | | | WANG Yadi | Radiation Oncology Department,the Seventh Medical Center of Chinese PLA General Hospital | | | Wang Qiusheng | School of Automation Science and Electrical Engineering,Beihang University | | | Zhang Fuli* | Radiation Oncology Department,the Seventh Medical Center of Chinese PLA General Hospital | 100700 |
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| 中文摘要: |
| 目的 介绍一种基于DenseNet深度学习网络的胸部CT危及器官端到端自动分割方法，提供一种高精度的自动分割模型，以减轻放射肿瘤医师手动勾画的工作量。方法 纳入36例肺癌CT图像，随机选取27例作为训练集，6例作为验证集，9例作为测试集。实现了左右肺、脊髓和心脏的自动分割任务，整个训练时间约为5小时。测试集通过几何指标评估，包括形状相似系数（DSC）、95%豪斯多夫距离（HD95）和平均表面距离（ASD）。然后，分别基于手动勾画靶区和危及器官以及自动勾画危及器官分别设计两组治疗计划。比较两组计划中危及器官的剂量学参数，包括 Dmax、Vx。结果 本文提出的深度学习网络的各几何学指标均优于U-Net网络，尽管差异无显著性意义（P>0.05）。与手动勾画相比，自动勾画可显著缩短近40.7%的时间（P<0.05）。此外，两组治疗计划的剂量-体积参数差异无统计学意义（P>0.05）。结论 基于DenseNet的深度学习方法可以准确地描绘出双侧肺、脊髓和心脏。特征图复用的思想为医学图像的自动勾画提供了新思路。 |
| 英文摘要: |
| Objective To introduce an end-to-end automatic segmentation method for organs at risk (OARs) in chest
computed tomography (CT) images based on dense connection deep learning and to provide an accurate
auto-segmentation model to reduce the workload on radiation oncologists.
Methods CT images of 36 lung cancer cases were included in this study. Of these, 27 cases were
randomly selected as the training set, six cases as the validation set, and nine cases as the testing set. The
left and right lungs, cord, and heart were auto-segmented, and the training time was set to approximately
5 h. The testing set was evaluated using geometric metrics including the Dice similarity coefficient (DSC),
95% Hausdorff distance (HD95), and average surface distance (ASD). Thereafter, two sets of treatment
plans were optimized based on manually contoured OARs and automatically contoured OARs, respectively.
Dosimetric parameters including Dmax and Vx of the OARs were obtained and compared.
Results The proposed model was superior to U-Net in terms of the DSC, HD95, and ASD, although there
was no significant difference in the segmentation results yielded by both networks (P > 0.05). Compared
to manual segmentation, auto-segmentation significantly reduced the segmentation time by nearly 40.7%
(P < 0.05). Moreover, the differences in dose-volume parameters between the two sets of plans were not
statistically significant (P > 0.05).
Conclusion The bilateral lung, cord, and heart could be accurately delineated using the DenseNetbased deep learning method. Thus, feature map reuse can be a novel approach to medical image autosegmentation . |
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