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020			_a9783030718275
024	7		_a10.1007/978-3-030-71827-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aRC78.7.D53 _b2021 EB
245	0	0	_aSegmentation, classification, and registration of multi-modality medical imaging data : _bMICCAI 2020 Challenges, ABCs 2020, L2R 2020, TN-SCUI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020, Proceedings _cedited by Nadya Shusharina, Mattias P Heinrich, Ruobing Huang
250			_aFirst edition 2021
264		1	_aCham, Switzerland _bSpringer International Publising _c2021
300			_a1 recurso en línea (XIX, 156 páginas) _b57 ilustraciones, 54 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 _v12587
505	0		_aABCs - Anatomical Brain Barriers to Cancer Spread: Segmentation from CT and MR Images -- Cross-modality Brain Structures Image Segmentation for the Radiotherapy Target Definition and Plan Optimization -- Domain Knowledge Driven Multi-modal Segmentation of Anatomical Brain Barriers to Cancer Spread -- Ensembled ResUnet for Anatomical Brain Barriers Segmentation -- An Enhanced Coarse-to-_ne Framework for the segmentation of clinical target volume -- Automatic Segmentation of brain structures for treatment planning optimization and target volume definition -- A Bi-Directional, Multi-Modality Framework for Segmentation of Brain Structures -- L2R - Learn2Reg: Multitask and Multimodal 3D Medical Image Registration -- Large Deformation Image Registration with Anatomy-aware Laplacian Pyramid Networks -- Discrete Unsupervised 3D Registration Methods for the Learn2Reg Challenge -- Variable Fraunhofer MEVIS RegLib comprehensively applied to Learn2Reg Challenge -- Learning a deformable registration pyramid -- Deep learning based registration using spatial gradients and noisy segmentation labels -- Multi-step, Learning-based, Semi-supervised Image Registration Algorithm -- Using Elastix to register inhale/exhale intrasubject thorax CT: a unsupervised baseline to the task 2 of the Learn2Reg challenge -- TN-SCUI - Thyroid Nodule Segmentation and Classification in Ultrasound Images -- Cascade Unet and CH-Unet for thyroid nodule segmenation and benign and malignant classification -- Identifying Thyroid Nodules in Ultrasound Images through Segmentation-guided Discriminative Localization -- Cascaded Networks for Thyroid Nodule Diagnosis from Ultrasound Images -- Automatic Segmentation and Classification of Thyroid Nodules in Ultrasound Images with Convolutional Neural Networks -- LRTHR-Net: A Low-Resolution-to-High-Resolution Framework to Iteratively Refine the Segmentation of Thyroid Nodule in Ultrasound Images -- Coarse to Fine Ensemble Network for Thyroid Nodule Segmentation.
520	3		_aThis book constitutes three challenges that were 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 Anatomical Brain Barriers to Cancer Spread: Segmentation from CT and MR Images Challenge, the Learn2Reg Challenge, and the Thyroid Nodule Segmentation and Classification in Ultrasound Images Challenge. The 19 papers presented in this volume were carefully reviewed and selected form numerous submissions. The ABCs challenge aims to identify the best methods of segmenting brain structures that serve as barriers to the spread of brain cancers and structures to be spared from irradiation, for use in computer assisted target definition for glioma and radiotherapy plan optimization. The papers of the L2R challenge cover a wide spectrum of conventional and learning-based registration methods and often describe novel contributions. The main goal of the TN-SCUI challenge is to find automatic algorithms to accurately segment and classify the thyroid nodules in ultrasound images. *The challenges took place virtually due to the COVID-19 pandemic.
988			_aSpringer_Computer_2021
650		7	_2embne _aDiagnóstico por imagen _9139972
700	1		_aShusharina, Nadya _eeditor literario _0(orcid)0000-0003-3041-2551 _1https://orcid.org/0000-0003-3041-2551 _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aHeinrich, Mattias P _eeditor literario _0(orcid)0000-0002-7489-1972 _1https://orcid.org/0000-0002-7489-1972 _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aHuang, Ruobing _eeditor literario _0(orcid)0000-0001-5672-6896 _1https://orcid.org/0000-0001-5672-6896 _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/10.1007/978-3-030-71827-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2021 _dz _eb _zSI