Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data
MICCAI 2020 Challenges, ABCs 2020, L2R 2020, TN-SCUI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020, Proceedings
Published on 13. March 2021
Book
Paperback/Softback
XIX, 156 pages
978-3-030-71826-8 (ISBN)
Description
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 tofind automatic algorithms to accurately segment and classify the thyroid nodules in ultrasound images.
*The challenges took place virtually due to the COVID-19 pandemic.
More details
Series
Edition
1st ed. 2021
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
3 s/w Abbildungen, 54 farbige Abbildungen
XIX, 156 p. 57 illus., 54 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 10 mm
Weight
277 gr
ISBN-13
978-3-030-71826-8 (9783030718268)
DOI
10.1007/978-3-030-71827-5
Schweitzer Classification
Other editions
Additional editions
Nadya Shusharina | Mattias P. Heinrich | Ruobing Huang
Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data
MICCAI 2020 Challenges, ABCs 2020, L2R 2020, TN-SCUI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020, Proceedings
E-Book
03/2021
Springer
€53.49
Available for download
Content
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.