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Updated: Feb 3, 2026
Deep Neural Networks for Image-Based Dietary Assessment
Published on: March 13, 2021
Comparison of the automatic segmentation of multiple organs at risk in CT images of lung cancer between deep
Jinhan Zhu1, Jun Zhang1, Bo Qiu1
1a State Key Laboratory of Oncology in South China , Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center , Guangzhou , China.
Background:
In this study, a deep convolutional neural network (CNN)-based automatic segmentation technique was applied to multiple organs at risk (OARs) depicted in computed tomography (CT) images of lung cancer patients, and the results were compared with those generated through atlas-based automatic segmentation.
Materials And Methods:
An encoder-decoder U-Net neural network was produced. The trained deep CNN performed the automatic segmentation of CT images for 36 cases of lung cancer. The Dice similarity coefficient (DSC), the mean surface distance (MSD) and the 95% Hausdorff distance (95% HD) were calculated, with manual segmentation results used as the standard, and were compared with the results obtained through atlas-based segmentation.
Results:
For the heart, lungs and liver, both the deep CNN-based and atlas-based techniques performed satisfactorily (average values: 0.87 < DSC < 0.95, 1.8 mm < MSD < 3.8 mm, 7.9 mm < 95% HD <11 mm). For the spinal cord and the oesophagus, the two methods had statistically significant differences. For the atlas-based technique, the average values were 0.54 < DSC < 0.71, 2.6 mm < MSD < 3.1 mm and 9.4 mm < 95% HD <12 mm. For the deep CNN-based technique, the average values were 0.71 < DSC < 0.79, 1.2 mm < MSD <2.2 mm and 4.0 mm < 95% HD < 7.9 mm.
Conclusion:
Our results showed that automatic segmentation based on a deep convolutional neural network enabled us to complete automatic segmentation tasks rapidly. Deep convolutional neural networks can be satisfactorily adapted to segment OARs during radiation treatment planning for lung cancer patients.
Insights
Deep convolutional neural networks (CNNs) offer rapid and accurate automatic segmentation of organs at risk in lung cancer CT scans. This AI-driven approach shows promise for improving radiation treatment planning.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Radiotherapy
Background:
- Automatic segmentation of organs at risk (OARs) is crucial for lung cancer radiation treatment planning.
- Traditional atlas-based methods face challenges in accuracy and efficiency.
Purpose of the Study:
- To evaluate a deep convolutional neural network (CNN)-based automatic segmentation technique for OARs in lung cancer CT images.
- To compare the performance of the deep CNN method against atlas-based segmentation.
Main Methods:
- An encoder-decoder U-Net deep CNN was developed and trained on CT images from 36 lung cancer patients.
- Segmentation accuracy was assessed using Dice Similarity Coefficient (DSC), Mean Surface Distance (MSD), and 95% Hausdorff Distance (95% HD) compared to manual segmentation.
Main Results:
- Deep CNN and atlas-based methods showed satisfactory results for the heart, lungs, and liver.
- Statistically significant differences were observed for the spinal cord and esophagus, with the deep CNN outperforming the atlas-based method (higher DSC, lower MSD and 95% HD).
Conclusions:
- Deep CNN-based automatic segmentation is a rapid and effective method for OAR segmentation in lung cancer patients.
- This AI technique is well-suited for optimizing radiation treatment planning.
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