Show simple item record

Toward a standard for the evaluation of PET‐Auto‐Segmentation methods following the recommendations of AAPM task group No. 211: Requirements and implementation

dc.contributor.authorBerthon, Beatrice
dc.contributor.authorSpezi, Emiliano
dc.contributor.authorGalavis, Paulina
dc.contributor.authorShepherd, Tony
dc.contributor.authorApte, Aditya
dc.contributor.authorHatt, Mathieu
dc.contributor.authorFayad, Hadi
dc.contributor.authorDe Bernardi, Elisabetta
dc.contributor.authorSoffientini, Chiara D.
dc.contributor.authorRoss Schmidtlein, C.
dc.contributor.authorEl Naqa, Issam
dc.contributor.authorJeraj, Robert
dc.contributor.authorLu, Wei
dc.contributor.authorDas, Shiva
dc.contributor.authorZaidi, Habib
dc.contributor.authorMawlawi, Osama R.
dc.contributor.authorVisvikis, Dimitris
dc.contributor.authorLee, John A.
dc.contributor.authorKirov, Assen S.
dc.date.accessioned2017-10-05T18:18:58Z
dc.date.available2018-11-01T16:42:00Zen
dc.date.issued2017-08
dc.identifier.citationBerthon, Beatrice; Spezi, Emiliano; Galavis, Paulina; Shepherd, Tony; Apte, Aditya; Hatt, Mathieu; Fayad, Hadi; De Bernardi, Elisabetta; Soffientini, Chiara D.; Ross Schmidtlein, C.; El Naqa, Issam; Jeraj, Robert; Lu, Wei; Das, Shiva; Zaidi, Habib; Mawlawi, Osama R.; Visvikis, Dimitris; Lee, John A.; Kirov, Assen S. (2017). "Toward a standard for the evaluation of PET‐Auto‐Segmentation methods following the recommendations of AAPM task group No. 211: Requirements and implementation." Medical Physics 44(8): 4098-4111.
dc.identifier.issn0094-2405
dc.identifier.issn2473-4209
dc.identifier.urihttps://hdl.handle.net/2027.42/138340
dc.publisherWiley Periodicals, Inc.
dc.subject.otherPET segmentation
dc.subject.otheroutlining assessment
dc.subject.otherconformity index
dc.subject.otherPET/CT
dc.titleToward a standard for the evaluation of PET‐Auto‐Segmentation methods following the recommendations of AAPM task group No. 211: Requirements and implementation
dc.typeArticleen_US
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelMedicine (General)
dc.subject.hlbtoplevelHealth Sciences
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/138340/1/mp12312_am.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/138340/2/mp12312.pdf
dc.identifier.doi10.1002/mp.12312
dc.identifier.sourceMedical Physics
dc.identifier.citedreferenceFrank SJ, Chao KSC, Schwartz DL, Weber RS, Apisarnthanarax S, Macapinlac HA. Technology insight: PET and PET/CT in head and neck tumor staging and radiation therapy planning. Nat Clin Pract Oncol. 2005; 2: 526 – 533.
dc.identifier.citedreferenceSiegel R, DeSantis C, Virgo K, et al. Cancer treatment and survivorship statistics, 2012. CA Cancer J Clin. 2012; 62: 220 – 241.
dc.identifier.citedreferenceChen K, Chen X. Positron emission tomography imaging of cancer biology: current status and future prospects. Semin Oncol. 2011; 38: 70 – 86.
dc.identifier.citedreferenceZaidi H, Vees H, Wissmeyer M. Molecular PET/CT‐guided radiation therapy treatment planning. Acad Radiol. 2009; 16: 1108 – 1133.
dc.identifier.citedreferencePaulino AC, Thorstad WL, Fox T. Role of fusion in radiotherapy treatment planning. Semin Nucl Med. 2003; 33: 238 – 243.
dc.identifier.citedreferenceAllozi R, Li XA, White J, et al. Tools for consensus analysis of experts’ contours for radiotherapy structure definitions. Radiother Oncol. 2010; 97: 572 – 578.
dc.identifier.citedreferenceLe Maitre A, Hatt M, Pradier O, Cheze‐le Rest C, Visvikis D. Impact of the accuracy of automatic tumour functional volume delineation on radiotherapy treatment planning. Phys Med Biol. 2012; 57: 5381 – 5397.
dc.identifier.citedreferenceBerthon B, Evans M, Marshall C, et al. Head and neck target delineation using a novel PET automatic segmentation algorithm. Radiother Oncol. 2017; 122: 242 – 247.
dc.identifier.citedreferenceBerthon B, Marshall C, Evans M, Spezi E. ATLAAS: an automatic decision tree‐based learning algorithm for advanced image segmentation in positron emission tomography. Phys Med Biol. 2016; 61: 4855 – 4869.
dc.identifier.citedreferenceBerthon B, Marshall C, Edwards A, Evans M, Spezi E. Influence of cold walls on PET image quantification and volume segmentation. Med Phys. 2013; 40: 1 – 13.
dc.identifier.citedreferenceSoffientini CD, De Bernardi E, Zito F, Castellani M, Baselli G. Background based Gaussian Mixture Model lesion segmentation in PET. Med Phys. 2016; 43: 2662 – 2675.
dc.identifier.citedreferenceHatt M, Cheze‐Lerest C, Turzo A, Roux C, Visvikis D. A fuzzy locally advanced bayesian segmentation approach for volume determination in PET. IEEE Trans Med Imaging. 2009; 28: 881 – 893.
dc.identifier.citedreferenceRapisarda E, Bettinardi V, Thielemans K, Gilardi MC. Image‐based point spread function implementation in a fully 3D OSEM reconstruction algorithm for PET. Phys Med Biol. 2010; 55: 4131 – 4151.
dc.identifier.citedreferenceGeets X, Lee JA, Bol A, Lonneux M, Grégoire V. A gradient‐based method for segmenting FDG‐PET images: methodology and validation. Eur J Nucl Med Mol Imaging. 2007; 34: 1427 – 1438.
dc.identifier.citedreferenceDubuisson M‐P, Jain AK, Lansing E, B AB. A modified hausdorff distance for object matching. Pattern Recogn. 1994; 1: 566 – 568.
dc.identifier.citedreferenceBerthon B, Häggström I, Apte A, et al. PETSTEP: generation of synthetic PET lesions for fast evaluation of segmentation methods. Phys Med. 2016; 31: 969 – 980.
dc.identifier.citedreferenceJan S, Santin G, Strul D, et al. GATE: a simulation toolkit for PET and SPECT. Phys Med Biol. 2004; 49: 4543 – 4561.
dc.identifier.citedreferenceHatt M, Cheze le Rest C, Descourt P, et al. Accurate automatic delineation of heterogeneous functional volumes in positron emission tomography for oncology applications. Int J Radiat Oncol Biol Phys. 2010; 77: 301 – 308.
dc.identifier.citedreferenceZito F, De Bernardi E, Soffientini C, et al. The use of zeolites to generate PET phantoms for the validation of quantification strategies in oncology. Med Phys. 2012; 39: 5353 – 5361.
dc.identifier.citedreferenceDaisne J‐F, Sibomana M, Bol A, Doumont T, Lonneux M, Grégoire V. Tri‐dimensional automatic segmentation of PET volumes based on measured source‐to‐background ratios: influence of reconstruction algorithms. Radiother Oncol. 2003; 69: 247 – 250.
dc.identifier.citedreferenceWanet M, Lee JA, Weynand B, et al. Gradient‐based delineation of the primary GTV on FDG‐PET in non‐small cell lung cancer: a comparison with threshold‐based approaches, CT and surgical specimens. Radiother Oncol. 2011; 98: 117 – 125.
dc.identifier.citedreferenceDeasy JO, Blanco AI, Clark VH. CERR: a computational environment for radiotherapy research. Med Phys. 2003; 30: 979 – 985.
dc.identifier.citedreferenceHatt M, Cheze Le Rest C, Albarghach N, Pradier O, Visvikis D. PET functional volume delineation: a robustness and repeatability study. Eur J Nucl Med Mol Imaging. 2011; 38: 663 – 672.
dc.identifier.citedreferenceUdupa JK, Leblanc VR, Zhuge Y, et al. A framework for evaluating image segmentation algorithms. Comput Med Imaging Graph. 2006; 30: 75 – 87.
dc.identifier.citedreferenceKirov AS, Fanchon LM. Pathology‐validated PET image data sets and their role in PET segmentation. Clin Transl Imaging. 2014; 2: 253 – 267.
dc.identifier.citedreferenceHatt M, Lee J, Schmidtlein CR, et al. Classification and evaluation strategies of auto‐segmentation approaches for PET: report of AAPM Task Group No. 211. Med Phys. 2017; 44: e1 – e42.
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.