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Thoracic Image Analysis : Second International Workshop, TIA 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings / edited by Jens Petersen, Raúl San José Estépar, Alexander Schmidt-Richberg, Sarah Gerard, Bianca Lassen-Schmidt, Colin Jacobs, Reinhard Beichel, Kensaku Mori.
- Format:
- Book
- Series:
- Computer Science (SpringerNature-11645)
- LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 12502
- Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 12502
- Language:
- English
- Subjects (All):
- Computer vision.
- Application software.
- Computers.
- Artificial intelligence.
- Computer Vision.
- Computer and Information Systems Applications.
- Computing Milieux.
- Artificial Intelligence.
- Local Subjects:
- Computer Vision.
- Computer and Information Systems Applications.
- Computing Milieux.
- Artificial Intelligence.
- Physical Description:
- 1 online resource (X, 166 pages) : 63 illustrations, 49 illustrations in color.
- Edition:
- 1st ed. 2020.
- Contained In:
- Springer Nature eBook
- Place of Publication:
- Cham : Springer International Publishing : Imprint: Springer, 2020.
- System Details:
- text file PDF
- Summary:
- This book constitutes the proceedings of the Second International Workshop on Thoracic Image Analysis, TIA 2020, held in Lima, Peru, in October 2020. Due to COVID-19 pandemic the conference was held virtually. COVID-19 infection has brought a lot of attention to lung imaging and the role of CT imaging in the diagnostic workflow of COVID-19 suspects is an important topic. The 14 full papers presented deal with all aspects of image analysis of thoracic data, including: image acquisition and reconstruction, segmentation, registration, quantification, visualization, validation, population-based modeling, biophysical modeling (computational anatomy), deep learning, image analysis in small animals, outcome-based research and novel infectious disease applications.
- Contents:
- Multi-cavity Heart Segmentation in Non-contrast Non-ECG Gated CT Scans with F-CNN
- 3D Deep Convolutional Neural Network-based Ventilated Lung Segmentation using Multi-nuclear Hyperpolarized Gas MRI
- Lung Cancer Tumor Region Segmentation Using Recurrent 3D-DenseUNet
- 3D Probabilistic Segmentation and Volumetry from 2D Projection Images
- CovidDiagnosis: Deep Diagnosis of Covid-19 Patients using Chest X-rays
- Can We Trust Deep Learning Based Diagnosis? The Impact of Domain Shift in Chest Radiograph Classification
- A Weakly Supervised Deep Learning Framework for COVID-19 CT Detection and Analysis
- Deep Reinforcement Learning for Localization of the Aortic Annulus in Patients with Aortic Dissection
- Functional-Consistent CycleGAN for CT to Iodine Perfusion Map Translation
- MRI to CTA Translation for Pulmonary Artery Evaluation using CycleGANs Trained with Unpaired Data
- Semi-supervised Virtual Regression of Aortic Dissections Using 3D Generative Inpainting
- Registration-Invariant Biomechanical Features for Disease Staging of COPD in SPIROMICS
- Deep Group-wise Variational Diffeomorphic Image Registration.
- Other Format:
- Printed edition:
- ISBN:
- 978-3-030-62469-9
- 9783030624699
- Access Restriction:
- Restricted for use by site license.
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