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 / edited by Nadya Shusharina, Mattias P. Heinrich, Ruobing Huang.

Format:

Book

Contributor:

Shusharina, Nadya, Editor.

Heinrich, Mattias P., Editor.

Huang, Ruobing, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 12587

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 12587

Language:

English

Subjects (All):

Image processing-Digital techniques.

Computer vision.

Artificial intelligence.

Bioinformatics.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Artificial Intelligence.

Computational and Systems Biology.

Local Subjects:

Computer Imaging, Vision, Pattern Recognition and Graphics.

Artificial Intelligence.

Computational and Systems Biology.

Physical Description:

1 online resource (XIX, 156 pages) : 57 illustrations, 54 illustrations in color.

Edition:

1st ed. 2021.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2021.

System Details:

text file PDF

Summary:

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.

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-030-71827-5

9783030718275

Access Restriction:

Restricted for use by site license.