ArticleMedicine2020
Comparison between atlas and convolutional neural network based automatic segmentation of multiple organs at risk in non-small cell lung cancer.
Article in Medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Comparative Analysis of Atlas and Neural Network Autosegmentation Methods for Pediatric Craniospinal Irradiation With the Development of a Knowledge-Based Quality Assurance Tool.Advances in radiation oncology · 2025Article
- Evaluating deep learning auto-contouring for lung radiation therapy: A review of accuracy, variability, efficiency and dose, in target volumes and organs at risk.Physics and imaging in radiation oncology · 2025Review
- A multicenter study on deep learning for glioblastoma auto-segmentation with prior knowledge in multimodal imaging.Cancer science · 2024Article
- Investigation of autosegmentation techniques on T2-weighted MRI for off-line dose reconstruction in MR-linac workflow for head and neck cancers.Medical physics · 2024Article
- A deep learning-based technique for the diagnosis of epidural spinal cord compression on thoracolumbar CT.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2023Article
- Deep learning based automatic segmentation of organs-at-risk for 0.35 T MRgRT of lung tumors.Radiation oncology (London, England) · 2023Article
- Machine learning in non-small cell lung cancer radiotherapy: A bibliometric analysis.Frontiers in oncology · 2023Article
- Applying a novel two-step deep learning network to improve the automatic delineation of esophagus in non-small cell lung cancer radiotherapy.Frontiers in oncology · 2023Article
- Research on Segmentation Technology in Lung Cancer Radiotherapy Based on Deep Learning.Current medical imaging · 2023Review
- Deep learning-based segmentation of the thorax in mouse micro-CT scans.Scientific reports · 2022Article
- 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.Frontiers in oncology · 2022Article
- Research on Management Efficiency and Dynamic Relationship in Intelligent Management of Tourism Engineering Based on Industry 4.0.Computational intelligence and neuroscience · 2022Article
- Review of Deep Learning Based Automatic Segmentation for Lung Cancer Radiotherapy.Frontiers in oncology · 2021Review
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7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Delineation of organs at risk (OARs) is important but time consuming for radiotherapy planning. Automatic segmentation of OARs based on convolutional neural network (CNN) has been established for lung cancer patients at our institution. The aim of this study is to compare automatic segmentation based on CNN (AS-CNN) with automatic segmentation based on atlas (AS-Atlas) in terms of the efficiency and accuracy of OARs contouring.The OARs, including the lungs, esophagus, heart, liver, and spinal cord, of 19 non-small cell lung cancer patients were delineated using three methods: AS-CNN, AS-Atlas in the Pinnacle-software, and manual delineation (MD) by a senior radiation oncologist. MD was used as the ground-truth reference, and the segmentation efficiency was evaluated by the time spent per patient. The accuracy was evaluated using the Mean surface distance (MSD) and Dice similarity coefficient (DSC). The paired t-test or Wilcoxon signed-rank test was used to compare these indexes between the 2 automatic segmentation models.In the 19 testing cases, both AS-CNN and AS-Atlas saved substantial time compared with MD. AS-CNN was more efficient than AS-Atlas (1.6 min vs 2.4 min, P < .001). In terms of the accuracy, AS-CNN performed well in the esophagus, with a DSC of 73.2%. AS-CNN was better than AS-Atlas in segmenting the left lung (DSC: 94.8% vs 93.2%, P = .01; MSD: 1.10 cm vs 1.73 cm, P < .001) and heart (DSC: 89.3% vs 85.8%, P = .05; MSD: 1.65 cm vs 3.66 cm, P < .001). Furthermore, AS-CNN exhibited superior performance in segmenting the liver (DSC: 93.7% vs 93.6%, P = .81; MSD: 2.03 cm VS 2.11 cm, P = .66). The results obtained from AS-CNN and AS-Atlas were similar in segmenting the right lung. However, the performance of AS-CNN in the spinal cord was inferior to that of AS-Atlas (DSC: 82.1% vs 86.8%, P = .01; MSD: 0.87 cm vs 0.66 cm, P = .01).Our study demonstrated that AS-CNN significantly reduced the contouring time and outperformed AS-Atlas in most cases. AS-CNN can potentially be used for OARs segmentation in patients with pathological N2 (pN2) non-small cell lung cancer.
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