Cardio-pulmonary substructure segmentation of radiotherapy computed tomography images using convolutional neural networks for clinical outcomes analysis Journal Article
| Authors: | Haq, R.; Hotca, A.; Apte, A.; Rimner, A.; Deasy, J. O.; Thor, M. |
| Article Title: | Cardio-pulmonary substructure segmentation of radiotherapy computed tomography images using convolutional neural networks for clinical outcomes analysis |
| Abstract: | Background and purpose: Radiation dose to the cardio-pulmonary system is critical for radiotherapy-induced mortality in non-small cell lung cancer. Our goal was to automatically segment substructures of the cardio-pulmonary system for use in outcomes analyses for thoracic cancers. We built and validated a multi-label Deep Learning Segmentation (DLS) model for accurate auto-segmentation of twelve cardio-pulmonary substructures. Materials and methods: The DLS model utilized a convolutional neural network for segmenting substructures from 217 thoracic radiotherapy Computed Tomography (CT) scans. The model was built in the presence of variable image characteristics such as the absence/presence of contrast. We quantitatively evaluated the final model against expert contours for a hold-out dataset of 24 CT scans using Dice Similarity Coefficient (DSC), 95th Percentile of Hausdorff Distance and Dose-volume Histograms (DVH). DLS contours of an additional 25 scans were qualitatively evaluated by a radiation oncologist to determine their clinical acceptability. Results: The DLS model reduced segmentation time per patient from about one hour to 10 s. Quantitatively, the highest accuracy was observed for the Heart (median DSC = (0.96 (0.95–0.97)). The median DSC for the remaining structures was between 0.81 and 0.93. No statistically significant difference was found between DVH metrics of the auto-generated and manual contours (p-value ⩾ 0.69). The expert judged that, on average, 85% of contours were qualitatively equivalent to state-of-the-art manual contouring. Conclusion: The cardio-pulmonary DLS model performed well both quantitatively and qualitatively for all structures. This model has been incorporated into an open-source tool for the community to use for treatment planning and clinical outcomes analysis. © 2020 |
| Keywords: | adult; treatment planning; outcome assessment; computer assisted tomography; radiotherapy; quantitative analysis; radiation therapy; heart; clinical outcome; dose volume histogram; clinical outcomes; human; article; radiation oncologist; deep learning; convolutional neural network; semantic segmentation; convolutional neural networks; cardio-pulmonary |
| Journal Title: | Physics and Imaging in Radiation Oncology |
| Volume: | 14 |
| ISSN: | 2405-6316 |
| Publisher: | Elsevier B.V. |
| Date Published: | 2020-04-01 |
| Start Page: | 61 |
| End Page: | 66 |
| Language: | English |
| DOI: | 10.1016/j.phro.2020.05.009 |
| PROVIDER: | scopus |
| PMCID: | PMC7807536 |
| PUBMED: | 33458316 |
| DOI/URL: | |
| Notes: | Article -- Source: Scopus |
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