An enhanced random walk algorithm for delineation of head and neck cancers in PET studies
Abstract An algorithm for delineating complex head and neck cancers in positron emission tomography (PET) images is presented in this article. An enhanced random walk (RW) algorithm with automatic seed detection is proposed and used to make the segmentation process feasible in the event of inhomogen...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Stefano, Alessandro [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Anmerkung: |
© International Federation for Medical and Biological Engineering 2016 |
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| Übergeordnetes Werk: |
Enthalten in: Medical & biological engineering & computing - Springer Berlin Heidelberg, 1977, 55(2016), 6 vom: 16. Sept., Seite 897-908 |
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| Übergeordnetes Werk: |
volume:55 ; year:2016 ; number:6 ; day:16 ; month:09 ; pages:897-908 |
| Links: |
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| DOI / URN: |
10.1007/s11517-016-1571-0 |
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| Katalog-ID: |
OLC2038696292 |
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| 520 | |a Abstract An algorithm for delineating complex head and neck cancers in positron emission tomography (PET) images is presented in this article. An enhanced random walk (RW) algorithm with automatic seed detection is proposed and used to make the segmentation process feasible in the event of inhomogeneous lesions with bifurcations. In addition, an adaptive probability threshold and a k-means based clustering technique have been integrated in the proposed enhanced RW algorithm. The new threshold is capable of following the intensity changes between adjacent slices along the whole cancer volume, leading to an operator-independent algorithm. Validation experiments were first conducted on phantom studies: High Dice similarity coefficients, high true positive volume fractions, and low Hausdorff distance confirm the accuracy of the proposed method. Subsequently, forty head and neck lesions were segmented in order to evaluate the clinical feasibility of the proposed approach against the most common segmentation algorithms. Experimental results show that the proposed algorithm is more accurate and robust than the most common algorithms in the literature. Finally, the proposed method also shows real-time performance, addressing the physician’s requirements in a radiotherapy environment. | ||
| 650 | 4 | |a Head and neck cancer segmentation | |
| 650 | 4 | |a Random walks | |
| 650 | 4 | |a PET imaging | |
| 650 | 4 | |a Biological target volume | |
| 700 | 1 | |a Vitabile, Salvatore |4 aut | |
| 700 | 1 | |a Russo, Giorgio |4 aut | |
| 700 | 1 | |a Ippolito, Massimo |4 aut | |
| 700 | 1 | |a Sabini, Maria Gabriella |4 aut | |
| 700 | 1 | |a Sardina, Daniele |4 aut | |
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| 700 | 1 | |a Pirrone, Roberto |4 aut | |
| 700 | 1 | |a Ardizzone, Edoardo |4 aut | |
| 700 | 1 | |a Gilardi, Maria Carla |4 aut | |
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10.1007/s11517-016-1571-0 doi (DE-627)OLC2038696292 (DE-He213)s11517-016-1571-0-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Stefano, Alessandro verfasserin (orcid)0000-0002-7189-1731 aut An enhanced random walk algorithm for delineation of head and neck cancers in PET studies 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © International Federation for Medical and Biological Engineering 2016 Abstract An algorithm for delineating complex head and neck cancers in positron emission tomography (PET) images is presented in this article. An enhanced random walk (RW) algorithm with automatic seed detection is proposed and used to make the segmentation process feasible in the event of inhomogeneous lesions with bifurcations. In addition, an adaptive probability threshold and a k-means based clustering technique have been integrated in the proposed enhanced RW algorithm. The new threshold is capable of following the intensity changes between adjacent slices along the whole cancer volume, leading to an operator-independent algorithm. Validation experiments were first conducted on phantom studies: High Dice similarity coefficients, high true positive volume fractions, and low Hausdorff distance confirm the accuracy of the proposed method. Subsequently, forty head and neck lesions were segmented in order to evaluate the clinical feasibility of the proposed approach against the most common segmentation algorithms. Experimental results show that the proposed algorithm is more accurate and robust than the most common algorithms in the literature. Finally, the proposed method also shows real-time performance, addressing the physician’s requirements in a radiotherapy environment. Head and neck cancer segmentation Random walks PET imaging Biological target volume Vitabile, Salvatore aut Russo, Giorgio aut Ippolito, Massimo aut Sabini, Maria Gabriella aut Sardina, Daniele aut Gambino, Orazio aut Pirrone, Roberto aut Ardizzone, Edoardo aut Gilardi, Maria Carla aut Enthalten in Medical & biological engineering & computing Springer Berlin Heidelberg, 1977 55(2016), 6 vom: 16. Sept., Seite 897-908 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:55 year:2016 number:6 day:16 month:09 pages:897-908 https://doi.org/10.1007/s11517-016-1571-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4219 AR 55 2016 6 16 09 897-908 |
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10.1007/s11517-016-1571-0 doi (DE-627)OLC2038696292 (DE-He213)s11517-016-1571-0-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Stefano, Alessandro verfasserin (orcid)0000-0002-7189-1731 aut An enhanced random walk algorithm for delineation of head and neck cancers in PET studies 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © International Federation for Medical and Biological Engineering 2016 Abstract An algorithm for delineating complex head and neck cancers in positron emission tomography (PET) images is presented in this article. An enhanced random walk (RW) algorithm with automatic seed detection is proposed and used to make the segmentation process feasible in the event of inhomogeneous lesions with bifurcations. In addition, an adaptive probability threshold and a k-means based clustering technique have been integrated in the proposed enhanced RW algorithm. The new threshold is capable of following the intensity changes between adjacent slices along the whole cancer volume, leading to an operator-independent algorithm. Validation experiments were first conducted on phantom studies: High Dice similarity coefficients, high true positive volume fractions, and low Hausdorff distance confirm the accuracy of the proposed method. Subsequently, forty head and neck lesions were segmented in order to evaluate the clinical feasibility of the proposed approach against the most common segmentation algorithms. Experimental results show that the proposed algorithm is more accurate and robust than the most common algorithms in the literature. Finally, the proposed method also shows real-time performance, addressing the physician’s requirements in a radiotherapy environment. Head and neck cancer segmentation Random walks PET imaging Biological target volume Vitabile, Salvatore aut Russo, Giorgio aut Ippolito, Massimo aut Sabini, Maria Gabriella aut Sardina, Daniele aut Gambino, Orazio aut Pirrone, Roberto aut Ardizzone, Edoardo aut Gilardi, Maria Carla aut Enthalten in Medical & biological engineering & computing Springer Berlin Heidelberg, 1977 55(2016), 6 vom: 16. Sept., Seite 897-908 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:55 year:2016 number:6 day:16 month:09 pages:897-908 https://doi.org/10.1007/s11517-016-1571-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4219 AR 55 2016 6 16 09 897-908 |
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10.1007/s11517-016-1571-0 doi (DE-627)OLC2038696292 (DE-He213)s11517-016-1571-0-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Stefano, Alessandro verfasserin (orcid)0000-0002-7189-1731 aut An enhanced random walk algorithm for delineation of head and neck cancers in PET studies 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © International Federation for Medical and Biological Engineering 2016 Abstract An algorithm for delineating complex head and neck cancers in positron emission tomography (PET) images is presented in this article. An enhanced random walk (RW) algorithm with automatic seed detection is proposed and used to make the segmentation process feasible in the event of inhomogeneous lesions with bifurcations. In addition, an adaptive probability threshold and a k-means based clustering technique have been integrated in the proposed enhanced RW algorithm. The new threshold is capable of following the intensity changes between adjacent slices along the whole cancer volume, leading to an operator-independent algorithm. Validation experiments were first conducted on phantom studies: High Dice similarity coefficients, high true positive volume fractions, and low Hausdorff distance confirm the accuracy of the proposed method. Subsequently, forty head and neck lesions were segmented in order to evaluate the clinical feasibility of the proposed approach against the most common segmentation algorithms. Experimental results show that the proposed algorithm is more accurate and robust than the most common algorithms in the literature. Finally, the proposed method also shows real-time performance, addressing the physician’s requirements in a radiotherapy environment. Head and neck cancer segmentation Random walks PET imaging Biological target volume Vitabile, Salvatore aut Russo, Giorgio aut Ippolito, Massimo aut Sabini, Maria Gabriella aut Sardina, Daniele aut Gambino, Orazio aut Pirrone, Roberto aut Ardizzone, Edoardo aut Gilardi, Maria Carla aut Enthalten in Medical & biological engineering & computing Springer Berlin Heidelberg, 1977 55(2016), 6 vom: 16. Sept., Seite 897-908 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:55 year:2016 number:6 day:16 month:09 pages:897-908 https://doi.org/10.1007/s11517-016-1571-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4219 AR 55 2016 6 16 09 897-908 |
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10.1007/s11517-016-1571-0 doi (DE-627)OLC2038696292 (DE-He213)s11517-016-1571-0-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Stefano, Alessandro verfasserin (orcid)0000-0002-7189-1731 aut An enhanced random walk algorithm for delineation of head and neck cancers in PET studies 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © International Federation for Medical and Biological Engineering 2016 Abstract An algorithm for delineating complex head and neck cancers in positron emission tomography (PET) images is presented in this article. An enhanced random walk (RW) algorithm with automatic seed detection is proposed and used to make the segmentation process feasible in the event of inhomogeneous lesions with bifurcations. In addition, an adaptive probability threshold and a k-means based clustering technique have been integrated in the proposed enhanced RW algorithm. The new threshold is capable of following the intensity changes between adjacent slices along the whole cancer volume, leading to an operator-independent algorithm. Validation experiments were first conducted on phantom studies: High Dice similarity coefficients, high true positive volume fractions, and low Hausdorff distance confirm the accuracy of the proposed method. Subsequently, forty head and neck lesions were segmented in order to evaluate the clinical feasibility of the proposed approach against the most common segmentation algorithms. Experimental results show that the proposed algorithm is more accurate and robust than the most common algorithms in the literature. Finally, the proposed method also shows real-time performance, addressing the physician’s requirements in a radiotherapy environment. Head and neck cancer segmentation Random walks PET imaging Biological target volume Vitabile, Salvatore aut Russo, Giorgio aut Ippolito, Massimo aut Sabini, Maria Gabriella aut Sardina, Daniele aut Gambino, Orazio aut Pirrone, Roberto aut Ardizzone, Edoardo aut Gilardi, Maria Carla aut Enthalten in Medical & biological engineering & computing Springer Berlin Heidelberg, 1977 55(2016), 6 vom: 16. Sept., Seite 897-908 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:55 year:2016 number:6 day:16 month:09 pages:897-908 https://doi.org/10.1007/s11517-016-1571-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4219 AR 55 2016 6 16 09 897-908 |
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10.1007/s11517-016-1571-0 doi (DE-627)OLC2038696292 (DE-He213)s11517-016-1571-0-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Stefano, Alessandro verfasserin (orcid)0000-0002-7189-1731 aut An enhanced random walk algorithm for delineation of head and neck cancers in PET studies 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © International Federation for Medical and Biological Engineering 2016 Abstract An algorithm for delineating complex head and neck cancers in positron emission tomography (PET) images is presented in this article. An enhanced random walk (RW) algorithm with automatic seed detection is proposed and used to make the segmentation process feasible in the event of inhomogeneous lesions with bifurcations. In addition, an adaptive probability threshold and a k-means based clustering technique have been integrated in the proposed enhanced RW algorithm. The new threshold is capable of following the intensity changes between adjacent slices along the whole cancer volume, leading to an operator-independent algorithm. Validation experiments were first conducted on phantom studies: High Dice similarity coefficients, high true positive volume fractions, and low Hausdorff distance confirm the accuracy of the proposed method. Subsequently, forty head and neck lesions were segmented in order to evaluate the clinical feasibility of the proposed approach against the most common segmentation algorithms. Experimental results show that the proposed algorithm is more accurate and robust than the most common algorithms in the literature. Finally, the proposed method also shows real-time performance, addressing the physician’s requirements in a radiotherapy environment. Head and neck cancer segmentation Random walks PET imaging Biological target volume Vitabile, Salvatore aut Russo, Giorgio aut Ippolito, Massimo aut Sabini, Maria Gabriella aut Sardina, Daniele aut Gambino, Orazio aut Pirrone, Roberto aut Ardizzone, Edoardo aut Gilardi, Maria Carla aut Enthalten in Medical & biological engineering & computing Springer Berlin Heidelberg, 1977 55(2016), 6 vom: 16. Sept., Seite 897-908 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:55 year:2016 number:6 day:16 month:09 pages:897-908 https://doi.org/10.1007/s11517-016-1571-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4219 AR 55 2016 6 16 09 897-908 |
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Enthalten in Medical & biological engineering & computing 55(2016), 6 vom: 16. Sept., Seite 897-908 volume:55 year:2016 number:6 day:16 month:09 pages:897-908 |
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An enhanced random walk algorithm for delineation of head and neck cancers in PET studies |
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An enhanced random walk algorithm for delineation of head and neck cancers in PET studies |
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Stefano, Alessandro |
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Stefano, Alessandro Vitabile, Salvatore Russo, Giorgio Ippolito, Massimo Sabini, Maria Gabriella Sardina, Daniele Gambino, Orazio Pirrone, Roberto Ardizzone, Edoardo Gilardi, Maria Carla |
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an enhanced random walk algorithm for delineation of head and neck cancers in pet studies |
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An enhanced random walk algorithm for delineation of head and neck cancers in PET studies |
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Abstract An algorithm for delineating complex head and neck cancers in positron emission tomography (PET) images is presented in this article. An enhanced random walk (RW) algorithm with automatic seed detection is proposed and used to make the segmentation process feasible in the event of inhomogeneous lesions with bifurcations. In addition, an adaptive probability threshold and a k-means based clustering technique have been integrated in the proposed enhanced RW algorithm. The new threshold is capable of following the intensity changes between adjacent slices along the whole cancer volume, leading to an operator-independent algorithm. Validation experiments were first conducted on phantom studies: High Dice similarity coefficients, high true positive volume fractions, and low Hausdorff distance confirm the accuracy of the proposed method. Subsequently, forty head and neck lesions were segmented in order to evaluate the clinical feasibility of the proposed approach against the most common segmentation algorithms. Experimental results show that the proposed algorithm is more accurate and robust than the most common algorithms in the literature. Finally, the proposed method also shows real-time performance, addressing the physician’s requirements in a radiotherapy environment. © International Federation for Medical and Biological Engineering 2016 |
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Abstract An algorithm for delineating complex head and neck cancers in positron emission tomography (PET) images is presented in this article. An enhanced random walk (RW) algorithm with automatic seed detection is proposed and used to make the segmentation process feasible in the event of inhomogeneous lesions with bifurcations. In addition, an adaptive probability threshold and a k-means based clustering technique have been integrated in the proposed enhanced RW algorithm. The new threshold is capable of following the intensity changes between adjacent slices along the whole cancer volume, leading to an operator-independent algorithm. Validation experiments were first conducted on phantom studies: High Dice similarity coefficients, high true positive volume fractions, and low Hausdorff distance confirm the accuracy of the proposed method. Subsequently, forty head and neck lesions were segmented in order to evaluate the clinical feasibility of the proposed approach against the most common segmentation algorithms. Experimental results show that the proposed algorithm is more accurate and robust than the most common algorithms in the literature. Finally, the proposed method also shows real-time performance, addressing the physician’s requirements in a radiotherapy environment. © International Federation for Medical and Biological Engineering 2016 |
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Abstract An algorithm for delineating complex head and neck cancers in positron emission tomography (PET) images is presented in this article. An enhanced random walk (RW) algorithm with automatic seed detection is proposed and used to make the segmentation process feasible in the event of inhomogeneous lesions with bifurcations. In addition, an adaptive probability threshold and a k-means based clustering technique have been integrated in the proposed enhanced RW algorithm. The new threshold is capable of following the intensity changes between adjacent slices along the whole cancer volume, leading to an operator-independent algorithm. Validation experiments were first conducted on phantom studies: High Dice similarity coefficients, high true positive volume fractions, and low Hausdorff distance confirm the accuracy of the proposed method. Subsequently, forty head and neck lesions were segmented in order to evaluate the clinical feasibility of the proposed approach against the most common segmentation algorithms. Experimental results show that the proposed algorithm is more accurate and robust than the most common algorithms in the literature. Finally, the proposed method also shows real-time performance, addressing the physician’s requirements in a radiotherapy environment. © International Federation for Medical and Biological Engineering 2016 |
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An enhanced random walk algorithm for delineation of head and neck cancers in PET studies |
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