國立高雄大學圖資館 |

Segmentation, classification, and registration of multi-modality medical imaging dataMICCAI 2020 Challenges, ABCs 2020, L2R 2020, TN-SCUI 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020 : proceedings /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Segmentation, classification, and registration of multi-modality medical imaging dataedited by Nadya Shusharina, Mattias P. Heinrich, Ruobing Huang.
其他題名:	MICCAI 2020 Challenges, ABCs 2020, L2R 2020, TN-SCUI 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020 : proceedings /
其他題名:	MICCAI 2020
其他作者:	Shusharina, Nadya.
團體作者:
出版者:	Cham :Springer International Publishing :2021.
面頁冊數:	xix, 156 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Diagnostic imagingCongresses.Data processing
電子資源:	https://doi.org/10.1007/978-3-030-71827-5
ISBN:	9783030718275$q(electronic bk.)

Segmentation, classification, and registration of multi-modality medical imaging dataMICCAI 2020 Challenges, ABCs 2020, L2R 2020, TN-SCUI 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020 : proceedings /
Segmentation, classification, and registration of multi-modality medical imaging dataMICCAI 2020 Challenges, ABCs 2020, L2R 2020, TN-SCUI 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020 : proceedings /[electronic resource] :MICCAI 2020edited by Nadya Shusharina, Mattias P. Heinrich, Ruobing Huang. - Cham :Springer International Publishing :2021. - xix, 156 p. :ill., digital ;24 cm. - Lecture notes in computer science,125870302-9743 ;. - Lecture notes in computer science ;4891..

ABCs - 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.

This 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.

ISBN: 9783030718275$q(electronic bk.)

Standard No.: 10.1007/978-3-030-71827-5doiSubjects--Topical Terms:

445765
Diagnostic imaging
--Data processing--Congresses.

LC Class. No.: RC78.7.D53 / I57 2020

Dewey Class. No.: 616.0754

Segmentation, classification, and registration of multi-modality medical imaging dataMICCAI 2020 Challenges, ABCs 2020, L2R 2020, TN-SCUI 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020 : proceedings /
LDR:04626nmm a2200397 a 4500 001 599313
003 DE-He213
005 20210312054245.0
006 m d
007 cr nn 008maaau
008 211027s2021 sz s 0 eng d
020 $a 9783030718275$q(electronic bk.)
020 $a 9783030718268$q(paper)
024 7 $a 10.1007/978-3-030-71827-5 $2 doi
035 $a 978-3-030-71827-5
040 $a GP $c GP
041 0 $a eng
050 4 $a RC78.7.D53 $b I57 2020
072 7 $a UYQV $2 bicssc
072 7 $a COM016000 $2 bisacsh
072 7 $a UYQV $2 thema
082 0 4 $a 616.0754 $2 23
090 $a RC78.7.D53 $b I61 2020
111 2 $n (3rd : $d 1998 : $c Amsterdam, Netherlands) $3 194767
245 1 0 $a Segmentation, classification, and registration of multi-modality medical imaging data $h [electronic resource] : $b MICCAI 2020 Challenges, ABCs 2020, L2R 2020, TN-SCUI 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020 : proceedings / $c edited by Nadya Shusharina, Mattias P. Heinrich, Ruobing Huang.
246 3 $a MICCAI 2020
246 3 $a ABCs 2020
246 3 $a L2R 2020
246 3 $a TN-SCUI 2020
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a xix, 156 p. : $b ill., digital ; $c 24 cm.
490 1 $a Lecture notes in computer science, $x 0302-9743 ; $v 12587
490 1 $a Image processing, computer vision, pattern recognition, and graphics
505 0 $a ABCs - 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 $a This 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.
650 0 $a Diagnostic imaging $x Data processing $v Congresses. $3 445765
650 1 4 $a Computer Imaging, Vision, Pattern Recognition and Graphics. $3 274492
650 2 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Computational Biology/Bioinformatics. $3 274833
700 1 $a Shusharina, Nadya. $3 893440
700 1 $a Heinrich, Mattias P. $3 893441
700 1 $a Huang, Ruobing. $3 893442
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Lecture notes in computer science ; $v 4891. $3 383229
830 0 $a Image processing, computer vision, pattern recognition, and graphics. $3 823073
856 4 0 $u https://doi.org/10.1007/978-3-030-71827-5
950 $a Computer Science (SpringerNature-11645)