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

Shusharina, Nadya (Editor)/ Heinrich, Mattias P. (Editor)/ Huang, Ruobing (Editor)

ISBN 10: 3030718263 / ISBN 13: 9783030718268

Edité par Springer-Nature New York Inc, 2021

A propos de cet article

175 pages. 9.25x6.10x0.38 inches. In Stock.

N° de réf. du vendeur x-3030718263

Détails bibliographiques

• Titre: 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
• Éditeur: Springer-Nature New York Inc
• Date de parution: 2021
• Langue: anglais
• ISBN à 10 chiffres: 3030718263
• ISBN à 13 chiffres: 9783030718268
• Reliure: Paperback
• État de l'article: Brand New

A propos de ce titre

Synopsis

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.