Thoracic Image Analysis (häftad)
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Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
166
Utgivningsdatum
2020-11-04
Upplaga
1st ed. 2020
Förlag
Springer Nature Switzerland AG
Medarbetare
Petersen, Jens (ed.), San José Estépar, Raul (ed.), Schmidt-Richberg, Alexander (ed.), Mori, Kensaku (ed.), Lassen-Schmidt, Bianca (ed.), Jacobs, Colin (ed.), Beichel, Reinhard (ed.), Gerard, Sarah (ed.)
Illustrationer
49 Illustrations, color; 14 Illustrations, black and white; X, 166 p. 63 illus., 49 illus. in color.
Dimensioner
234 x 156 x 10 mm
Vikt
259 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISBN
9783030624682

Thoracic Image Analysis

Second International Workshop, TIA 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings

Häftad,  Engelska, 2020-11-04
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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.
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Innehållsförteckning

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.