Towards Automation in Radiotherapy Planning: A Deep Learning Approach for the Delineation of Parotid Glands in Head and Neck Cancer
The delineation of parotid glands in head and neck (HN) carcinoma is critical to assess radiotherapy (RT) planning. Segmentation processes ensure precise target position and treatment precision, facilitate monitoring of anatomical changes, enable plan adaptation, and enhance overall patient safety....
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/11/3/214 |
| _version_ | 1797242034102206464 |
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| author | Ioannis Kakkos Theodoros P. Vagenas Anna Zygogianni George K. Matsopoulos |
| author_facet | Ioannis Kakkos Theodoros P. Vagenas Anna Zygogianni George K. Matsopoulos |
| author_sort | Ioannis Kakkos |
| collection | DOAJ |
| description | The delineation of parotid glands in head and neck (HN) carcinoma is critical to assess radiotherapy (RT) planning. Segmentation processes ensure precise target position and treatment precision, facilitate monitoring of anatomical changes, enable plan adaptation, and enhance overall patient safety. In this context, artificial intelligence (AI) and deep learning (DL) have proven exceedingly effective in precisely outlining tumor tissues and, by extension, the organs at risk. This paper introduces a DL framework using the AttentionUNet neural network for automatic parotid gland segmentation in HN cancer. Extensive evaluation of the model is performed in two public and one private dataset, while segmentation accuracy is compared with other state-of-the-art DL segmentation schemas. To assess replanning necessity during treatment, an additional registration method is implemented on the segmentation output, aligning images of different modalities (Computed Tomography (CT) and Cone Beam CT (CBCT)). AttentionUNet outperforms similar DL methods (Dice Similarity Coefficient: 82.65% ± 1.03, Hausdorff Distance: 6.24 mm ± 2.47), confirming its effectiveness. Moreover, the subsequent registration procedure displays increased similarity, providing insights into the effects of RT procedures for treatment planning adaptations. The implementation of the proposed methods indicates the effectiveness of DL not only for automatic delineation of the anatomical structures, but also for the provision of information for adaptive RT support. |
| first_indexed | 2024-04-24T18:32:48Z |
| format | Article |
| id | doaj.art-e261a2e5ec96425887c8aa67f8ebae45 |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-04-24T18:32:48Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-e261a2e5ec96425887c8aa67f8ebae452024-03-27T13:21:47ZengMDPI AGBioengineering2306-53542024-02-0111321410.3390/bioengineering11030214Towards Automation in Radiotherapy Planning: A Deep Learning Approach for the Delineation of Parotid Glands in Head and Neck CancerIoannis Kakkos0Theodoros P. Vagenas1Anna Zygogianni2George K. Matsopoulos3Biomedical Engineering Laboratory, National Technical University of Athens, 15773 Athens, GreeceBiomedical Engineering Laboratory, National Technical University of Athens, 15773 Athens, GreeceRadiation Oncology Unit, 1st Department of Radiology, ARETAIEION University Hospital, 11528 Athens, GreeceBiomedical Engineering Laboratory, National Technical University of Athens, 15773 Athens, GreeceThe delineation of parotid glands in head and neck (HN) carcinoma is critical to assess radiotherapy (RT) planning. Segmentation processes ensure precise target position and treatment precision, facilitate monitoring of anatomical changes, enable plan adaptation, and enhance overall patient safety. In this context, artificial intelligence (AI) and deep learning (DL) have proven exceedingly effective in precisely outlining tumor tissues and, by extension, the organs at risk. This paper introduces a DL framework using the AttentionUNet neural network for automatic parotid gland segmentation in HN cancer. Extensive evaluation of the model is performed in two public and one private dataset, while segmentation accuracy is compared with other state-of-the-art DL segmentation schemas. To assess replanning necessity during treatment, an additional registration method is implemented on the segmentation output, aligning images of different modalities (Computed Tomography (CT) and Cone Beam CT (CBCT)). AttentionUNet outperforms similar DL methods (Dice Similarity Coefficient: 82.65% ± 1.03, Hausdorff Distance: 6.24 mm ± 2.47), confirming its effectiveness. Moreover, the subsequent registration procedure displays increased similarity, providing insights into the effects of RT procedures for treatment planning adaptations. The implementation of the proposed methods indicates the effectiveness of DL not only for automatic delineation of the anatomical structures, but also for the provision of information for adaptive RT support.https://www.mdpi.com/2306-5354/11/3/214head and neck cancerCTparotid glandsartificial intelligencedeep learningradiation therapy |
| spellingShingle | Ioannis Kakkos Theodoros P. Vagenas Anna Zygogianni George K. Matsopoulos Towards Automation in Radiotherapy Planning: A Deep Learning Approach for the Delineation of Parotid Glands in Head and Neck Cancer Bioengineering head and neck cancer CT parotid glands artificial intelligence deep learning radiation therapy |
| title | Towards Automation in Radiotherapy Planning: A Deep Learning Approach for the Delineation of Parotid Glands in Head and Neck Cancer |
| title_full | Towards Automation in Radiotherapy Planning: A Deep Learning Approach for the Delineation of Parotid Glands in Head and Neck Cancer |
| title_fullStr | Towards Automation in Radiotherapy Planning: A Deep Learning Approach for the Delineation of Parotid Glands in Head and Neck Cancer |
| title_full_unstemmed | Towards Automation in Radiotherapy Planning: A Deep Learning Approach for the Delineation of Parotid Glands in Head and Neck Cancer |
| title_short | Towards Automation in Radiotherapy Planning: A Deep Learning Approach for the Delineation of Parotid Glands in Head and Neck Cancer |
| title_sort | towards automation in radiotherapy planning a deep learning approach for the delineation of parotid glands in head and neck cancer |
| topic | head and neck cancer CT parotid glands artificial intelligence deep learning radiation therapy |
| url | https://www.mdpi.com/2306-5354/11/3/214 |
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