Neural-network based autocontouring algorithm for intrafractional lung-tumor tracking using Linac-MR.
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| Title: | Neural-network based autocontouring algorithm for intrafractional lung-tumor tracking using Linac-MR. |
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| Authors: | Yun, Jihyun1, Yip, Eugene1, Gabos, Zsolt2, Wachowicz, Keith3, Rathee, Satyapal3, Fallone, B. G.4 |
| Source: | Medical Physics. May2015, Vol. 42 Issue 5, p2296-2310. 15p. |
| Subjects: | Artificial neural networks, Algorithms, Lung cancer diagnosis, Magnetic resonance imaging of cancer, Hausdorff measures |
| Abstract: | Purpose: To develop a neural-network based autocontouring algorithm for intrafractional lung-tumor tracking using Linac-MR and evaluate its performance with phantom and in-vivo MR images. Methods: An autocontouring algorithm was developed to determine both the shape and position of a lung tumor from each intrafractional MR image. A pulse-coupled neural network was implemented in the algorithm for contrast improvement of the tumor region. Prior to treatment, to initiate the algorithm, an expert user needs to contour the tumor and its maximum anticipated range of motion in pretreatment MR images. During treatment, however, the algorithm processes each intrafractional MR image and automatically generates a tumor contour without further user input. The algorithm is designed to produce a tumor contour that is the most similar to the expert's manual one. To evaluate the autocontouring algorithm in the author's Linac-MR environment which utilizes a 0.5 T MRI, a motion phantom and four lung cancer patients were imaged with 3 T MRI during normal breathing, and the image noise was degraded to reflect the image noise at 0.5 T. Each of the pseudo-0.5 T images was autocontoured using the author's algorithm. In each test image, the Dice similarity index (DSI) and Hausdorff distance (HD) between the expert's manual contour and the algorithm generated contour were calculated, and their centroid positions were compared (Δdcentroid). Results: The algorithm successfully contoured the shape of a moving tumor from dynamic MR images acquired every 275 ms. From the phantom study, mean DSI of 0.95-0.96, mean HD of 2.61-2.82 mm, and mean Δdcentroid of 0.68-0.93 mm were achieved. From the in-vivo study, the author's algorithm achieved mean DSI of 0.87-0.92, mean HD of 3.12-4.35 mm, as well as Δdcentroid of 1.03-1.35 mm. Autocontouring speed was less than 20 ms for each image. Conclusions: The authors have developed and evaluated a lung tumor autocontouring algorithm for intrafractional tumor tracking using Linac-MR. The autocontouring performance in the Linac-MR environment was evaluated using phantom and in-vivo MR images. From the in-vivo study, the author's algorithm achieved 87%-92% of contouring agreement and centroid tracking accuracy of 1.03-1.35 mm. These results demonstrate the feasibility of lung tumor autocontouring in the author's laboratory's Linac-MR environment. [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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| Header | DbId: egs DbLabel: Engineering Source An: 102751278 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Neural-network based autocontouring algorithm for intrafractional lung-tumor tracking using Linac-MR. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yun%2C+Jihyun%22">Yun, Jihyun</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Yip%2C+Eugene%22">Yip, Eugene</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Gabos%2C+Zsolt%22">Gabos, Zsolt</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Wachowicz%2C+Keith%22">Wachowicz, Keith</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Rathee%2C+Satyapal%22">Rathee, Satyapal</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Fallone%2C+B%2E+G%2E%22">Fallone, B. G.</searchLink><relatesTo>4</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Medical+Physics%22">Medical Physics</searchLink>. May2015, Vol. 42 Issue 5, p2296-2310. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Lung+cancer+diagnosis%22">Lung cancer diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetic+resonance+imaging+of+cancer%22">Magnetic resonance imaging of cancer</searchLink><br /><searchLink fieldCode="DE" term="%22Hausdorff+measures%22">Hausdorff measures</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Purpose: To develop a neural-network based autocontouring algorithm for intrafractional lung-tumor tracking using Linac-MR and evaluate its performance with phantom and in-vivo MR images. Methods: An autocontouring algorithm was developed to determine both the shape and position of a lung tumor from each intrafractional MR image. A pulse-coupled neural network was implemented in the algorithm for contrast improvement of the tumor region. Prior to treatment, to initiate the algorithm, an expert user needs to contour the tumor and its maximum anticipated range of motion in pretreatment MR images. During treatment, however, the algorithm processes each intrafractional MR image and automatically generates a tumor contour without further user input. The algorithm is designed to produce a tumor contour that is the most similar to the expert's manual one. To evaluate the autocontouring algorithm in the author's Linac-MR environment which utilizes a 0.5 T MRI, a motion phantom and four lung cancer patients were imaged with 3 T MRI during normal breathing, and the image noise was degraded to reflect the image noise at 0.5 T. Each of the pseudo-0.5 T images was autocontoured using the author's algorithm. In each test image, the Dice similarity index (DSI) and Hausdorff distance (HD) between the expert's manual contour and the algorithm generated contour were calculated, and their centroid positions were compared (Δdcentroid). Results: The algorithm successfully contoured the shape of a moving tumor from dynamic MR images acquired every 275 ms. From the phantom study, mean DSI of 0.95-0.96, mean HD of 2.61-2.82 mm, and mean Δdcentroid of 0.68-0.93 mm were achieved. From the in-vivo study, the author's algorithm achieved mean DSI of 0.87-0.92, mean HD of 3.12-4.35 mm, as well as Δdcentroid of 1.03-1.35 mm. Autocontouring speed was less than 20 ms for each image. Conclusions: The authors have developed and evaluated a lung tumor autocontouring algorithm for intrafractional tumor tracking using Linac-MR. The autocontouring performance in the Linac-MR environment was evaluated using phantom and in-vivo MR images. From the in-vivo study, the author's algorithm achieved 87%-92% of contouring agreement and centroid tracking accuracy of 1.03-1.35 mm. These results demonstrate the feasibility of lung tumor autocontouring in the author's laboratory's Linac-MR environment. [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: 10.1118/1.4916657 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 2296 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Lung cancer diagnosis Type: general – SubjectFull: Magnetic resonance imaging of cancer Type: general – SubjectFull: Hausdorff measures Type: general Titles: – TitleFull: Neural-network based autocontouring algorithm for intrafractional lung-tumor tracking using Linac-MR. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yun, Jihyun – PersonEntity: Name: NameFull: Yip, Eugene – PersonEntity: Name: NameFull: Gabos, Zsolt – PersonEntity: Name: NameFull: Wachowicz, Keith – PersonEntity: Name: NameFull: Rathee, Satyapal – PersonEntity: Name: NameFull: Fallone, B. G. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2015 Type: published Y: 2015 Identifiers: – Type: issn-print Value: 00942405 Numbering: – Type: volume Value: 42 – Type: issue Value: 5 Titles: – TitleFull: Medical Physics Type: main |
| ResultId | 1 |
