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008			210112s2021 gw a o |1|| 0|eng d
020			_a9783030671945
024	7		_a10.1007/978-3-030-67194-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTA1630 _b2021 EB
245	0	0	_aHead and neck tumor segmentation : _bFirst Challenge, HECKTOR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings _cedited by Vincent Andrearczyk, Valentin Oreiller, Adrien Depeursinge
250			_aFirst edition 2021
264		1	_aCham, Switzerland _bSpringer International Publising _c2021
300			_a1 recurso en línea (X, 109 páginas) _b32 ilustraciones, 29 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2rda
490	0		_aImage Processing Computer Vision Pattern Recognition and Graphics _v12603
505	0		_aOverview of the HECKTOR Challenge at MICCAI 2020: Automatic Head and Neck Tumor Segmentation in PET/CT -- Two-stage approach for segmenting gross tumor volume in head and neck cancer with CT and PET imaging -- The Head and Neck Tumor Segmentation Using nnU-Net with Spatial and Channel 'Squeeze & Excitation' Blocks -- Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT Images -- Automatic Head and Neck Tumor Segmentation in PET/CT with Scale Attention Network -- Iteratively Refine the Segmentation of Head and Neck Tumor in FDG-PET and CT images -- Combining CNN and Hybrid Active Contours for Head and Neck Tumor Segmentation in CT and PET images -- Oropharyngeal Tumour Segmentation using Ensemble 3D PET-CT Fusion Networks for the HECKTOR Challenge -- Patch-based 3D UNet for Head and Neck Tumor Segmentation with an Ensemble of Conventional and Dilated Convolutions -- Tumor Segmentation in Patients with Head and Neck Cancers using Deep Learning based-on Multi-modality PET/CT Images -- GAN-based Bi-modal Segmentation using Mumford-Shah Loss: Application to Head and Neck Tumors in PET-CT Images.
520	3		_aThis book constitutes the First 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2020, which was held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020. The challenge took place virtually due to the COVID-19 pandemic. The 2 full and 8 short papers presented together with an overview paper in this volume were carefully reviewed and selected form numerous submissions. This challenge aims to evaluate and compare the current state-of-the-art methods for automatic head and neck tumor segmentation. In the context of this challenge, a dataset of 204 delineated PET/CT images was made available for training as well as 53 PET/CT images for testing. Various deep learning methods were developed by the participants with excellent results.
988			_aSpringer_Computer_2021
650		7	_2embne _9669495 _aProceso de imágenes _xTécnicas digitales _vCongresos y asambleas
650		7	_2embne _9421371 _aInteligencia artificial en medicina _vCongresos y asambleas
650		7	_2embne _9174085 _aCáncer _xTratamiento _vCongresos y asambleas
700	1		_aAndrearczyk, Vincent _eeditor literario _0(orcid)0000-0003-0793-5821 _1https://orcid.org/0000-0003-0793-5821 _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aOreiller, Valentin _eeditor literario _0(orcid)0000-0002-7794-6916 _1https://orcid.org/0000-0002-7794-6916 _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aDepeursinge, Adrien _eeditor literario _0(orcid)0000-0002-2362-0304 _1https://orcid.org/0000-0002-2362-0304 _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-67194-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2021 _dz _eb _zSI