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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

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...

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Bibliographic Details
Other Authors: Shusharina, Nadya (Editor), Heinrich, Mattias P. (Editor), Huang, Ruobing (Editor)
Format: eBook
Language:English
Published: Cham Springer International Publishing 2021, 2021
Edition:1st ed. 2021
Series:Image Processing, Computer Vision, Pattern Recognition, and Graphics
Subjects:
Image Processing / Digital Techniques
Bioinformatics
Computational And Systems Biology
Computer Vision
Artificial Intelligence
Computer Imaging, Vision, Pattern Recognition And Graphics
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
Table of Contents:
  • Coarse to Fine Ensemble Network for Thyroid Nodule Segmentation
  • 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
  • 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

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