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001			9780429323782
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008			210227s2021 flu ob 001 0 eng d
040			_aOCoLC-P _beng _cOCoLC-P
020			_a9781000376340 _q(electronic bk.)
020			_a1000376346 _q(electronic bk.)
020			_a9781000376302 _q(e-book)
020			_a1000376303
020			_a9780429323782 _q(ebook)
020			_a0429323786
020			_z9780367336004
020			_z0367336006
024	8		_a10.1201/9780429323782 _2doi
035			_a(OCoLC)1239980122
035			_a(OCoLC-P)1239980122
050		4	_aRC271.R3
072		7	_aSCI _x055000 _2bisacsh
072		7	_aSCI _x058000 _2bisacsh
072		7	_aMED _x062000 _2bisacsh
072		7	_aPHVD _2bicssc
082	0	4	_a616.9940642 _223
245	0	0	_aAuto-segmentation for radiation oncology _h[electronic resource] : _bstate of the art / _cedited by Jinzhong Yang, Gregory C. Sharp, Mark J. Gooding.
264		1	_aBoca Raton : _bCRC Press, _c2021.
300			_a1 online resource.
336			_atext _2rdacontent
336			_astill image _2rdacontent
337			_acomputer _2rdamedia
338			_aonline resource _2rdacarrier
490	0		_aSeries in medical and biomedical engineering
520			_aThis book provides a comprehensive introduction to current state-of-the-art auto-segmentation approaches used in radiation oncology for auto-delineation of organs-of-risk for thoracic radiation treatment planning. Containing the latest, cutting edge technologies and treatments, it explores deep-learning methods, multi-atlas-based methods, and model-based methods that are currently being developed for clinical radiation oncology applications. Each chapter focuses on a specific aspect of algorithm choices and discusses the impact of the different algorithm modules to the algorithm performance as well as the implementation issues for clinical use (including data curation challenges and auto-contour evaluations). This book is an ideal guide for radiation oncology centers looking to learn more about potential auto-segmentation tools for their clinic in addition to medical physicists commissioning auto-segmentation for clinical use. Features: Up-to-date with the latest technologies in the field Edited by leading authorities in the area, with chapter contributions from subject area specialists All approaches presented in this book are validated using a standard benchmark dataset established by the Thoracic Auto-segmentation Challenge held as an event of the 2017 Annual Meeting of American Association of Physicists in Medicine
588			_aOCLC-licensed vendor bibliographic record.
650		0	_aCancer _xRadiotherapy.
650		0	_aMedical physics.
650		7	_aSCIENCE / Physics _2bisacsh
650		7	_aSCIENCE / Radiation _2bisacsh
650		7	_aMEDICAL / Oncology _2bisacsh
700	1		_aYang, Jinzhong _c(Professor of Radiation Physics), _eeditor.
856	4	0	_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9780429323782
856	4	2	_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
999			_c70508 _d70508