A multimodality segmentation framework for automatic target delineation in head and neck radiotherapy.
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| Title: | A multimodality segmentation framework for automatic target delineation in head and neck radiotherapy. |
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| Authors: | Yang, Jinzhong1, Beadle, Beth M.2, Garden, Adam S.2, Schwartz, David L.3, Aristophanous, Michalis1 |
| Source: | Medical Physics. Sep2015, Vol. 42 Issue 9, p5310-5320. 11p. |
| Subjects: | Head & neck cancer, Image segmentation, Automatic target recognition, Cancer radiotherapy, Image quality in imaging systems, Cancer tomography |
| Abstract: | Purpose: To develop an automatic segmentation algorithm integrating imaging information from computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) to delineate target volume in head and neck cancer radiotherapy. Methods: Eleven patients with unresectable disease at the tonsil or base of tongue who underwent MRI, CT, and PET/CT within two months before the start of radiotherapy or chemoradiotherapy were recruited for the study. For each patient, PET/CT and T1-weighted contrast MRI scans were first registered to the planning CT using deformable and rigid registration, respectively, to resample the PET and magnetic resonance (MR) images to the planning CT space. A binary mask was manually defined to identify the tumor area. The resampled PET and MR images, the planning CT image, and the binary mask were fed into the automatic segmentation algorithm for target delineation. The algorithm was based on a multichannel Gaussian mixture model and solved using an expectation-maximization algorithm with Markov random fields. To evaluate the algorithm, we compared the multichannel autosegmentation with an autosegmentation method using only PET images. The physician-defined gross tumor volume (GTV) was used as the "ground truth" for quantitative evaluation. Results: The median multichannel segmented GTV of the primary tumor was 15.7 cm³ (range, 6.6-44.3 cm³), while the PET segmented GTV was 10.2 cm³ (range, 2.8-45.1 cm³). The median physician-defined GTV was 22.1 cm³ (range, 4.2-38.4 cm³). The median difference between the multichannel segmented and physician-defined GTVs was -10.7%, not showing a statistically significant difference (p-value = 0.43). However, the median difference between the PET segmented and physician-defined GTVs was -19.2%, showing a statistically significant difference (p-value = 0.0037). The median Dice similarity coefficient between the multichannel segmented and physician-defined GTVs was 0.75 (range, 0.55-0.84), and the median sensitivity and positive predictive value between them were 0.76 and 0.81, respectively. Conclusions: The authors developed an automated multimodality segmentation algorithm for tumor volume delineation and validated this algorithm for head and neck cancer radiotherapy. The multichannel segmented GTV agreed well with the physician-defined GTV. The authors expect that their algorithm will improve the accuracy and consistency in target definition for radiotherapy. [ABSTRACT FROM AUTHOR] |
| Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Items | – Name: Title Label: Title Group: Ti Data: A multimodality segmentation framework for automatic target delineation in head and neck radiotherapy. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yang%2C+Jinzhong%22">Yang, Jinzhong</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Beadle%2C+Beth+M%2E%22">Beadle, Beth M.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Garden%2C+Adam+S%2E%22">Garden, Adam S.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Schwartz%2C+David+L%2E%22">Schwartz, David L.</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Aristophanous%2C+Michalis%22">Aristophanous, Michalis</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Medical+Physics%22">Medical Physics</searchLink>. Sep2015, Vol. 42 Issue 9, p5310-5320. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Head+%26+neck+cancer%22">Head & neck cancer</searchLink><br /><searchLink fieldCode="DE" term="%22Image+segmentation%22">Image segmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Automatic+target+recognition%22">Automatic target recognition</searchLink><br /><searchLink fieldCode="DE" term="%22Cancer+radiotherapy%22">Cancer radiotherapy</searchLink><br /><searchLink fieldCode="DE" term="%22Image+quality+in+imaging+systems%22">Image quality in imaging systems</searchLink><br /><searchLink fieldCode="DE" term="%22Cancer+tomography%22">Cancer tomography</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Purpose: To develop an automatic segmentation algorithm integrating imaging information from computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) to delineate target volume in head and neck cancer radiotherapy. Methods: Eleven patients with unresectable disease at the tonsil or base of tongue who underwent MRI, CT, and PET/CT within two months before the start of radiotherapy or chemoradiotherapy were recruited for the study. For each patient, PET/CT and T1-weighted contrast MRI scans were first registered to the planning CT using deformable and rigid registration, respectively, to resample the PET and magnetic resonance (MR) images to the planning CT space. A binary mask was manually defined to identify the tumor area. The resampled PET and MR images, the planning CT image, and the binary mask were fed into the automatic segmentation algorithm for target delineation. The algorithm was based on a multichannel Gaussian mixture model and solved using an expectation-maximization algorithm with Markov random fields. To evaluate the algorithm, we compared the multichannel autosegmentation with an autosegmentation method using only PET images. The physician-defined gross tumor volume (GTV) was used as the "ground truth" for quantitative evaluation. Results: The median multichannel segmented GTV of the primary tumor was 15.7 cm³ (range, 6.6-44.3 cm³), while the PET segmented GTV was 10.2 cm³ (range, 2.8-45.1 cm³). The median physician-defined GTV was 22.1 cm³ (range, 4.2-38.4 cm³). The median difference between the multichannel segmented and physician-defined GTVs was -10.7%, not showing a statistically significant difference (p-value = 0.43). However, the median difference between the PET segmented and physician-defined GTVs was -19.2%, showing a statistically significant difference (p-value = 0.0037). The median Dice similarity coefficient between the multichannel segmented and physician-defined GTVs was 0.75 (range, 0.55-0.84), and the median sensitivity and positive predictive value between them were 0.76 and 0.81, respectively. Conclusions: The authors developed an automated multimodality segmentation algorithm for tumor volume delineation and validated this algorithm for head and neck cancer radiotherapy. The multichannel segmented GTV agreed well with the physician-defined GTV. The authors expect that their algorithm will improve the accuracy and consistency in target definition for radiotherapy. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1118/1.4928485 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 5310 Subjects: – SubjectFull: Head & neck cancer Type: general – SubjectFull: Image segmentation Type: general – SubjectFull: Automatic target recognition Type: general – SubjectFull: Cancer radiotherapy Type: general – SubjectFull: Image quality in imaging systems Type: general – SubjectFull: Cancer tomography Type: general Titles: – TitleFull: A multimodality segmentation framework for automatic target delineation in head and neck radiotherapy. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yang, Jinzhong – PersonEntity: Name: NameFull: Beadle, Beth M. – PersonEntity: Name: NameFull: Garden, Adam S. – PersonEntity: Name: NameFull: Schwartz, David L. – PersonEntity: Name: NameFull: Aristophanous, Michalis IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2015 Type: published Y: 2015 Identifiers: – Type: issn-print Value: 00942405 Numbering: – Type: volume Value: 42 – Type: issue Value: 9 Titles: – TitleFull: Medical Physics Type: main |
| ResultId | 1 |
