Thoracic Image Analysis -

Thoracic Image Analysis

Second International Workshop, TIA 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings
Buch | Softcover
X, 166 Seiten
2020 | 1st ed. 2020
Springer International Publishing (Verlag)
978-3-030-62468-2 (ISBN)
53,49 inkl. MwSt
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.

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.


Erscheinungsdatum
Reihe/Serie Image Processing, Computer Vision, Pattern Recognition, and Graphics
Lecture Notes in Computer Science
Zusatzinfo X, 166 p. 63 illus., 49 illus. in color.
Verlagsort Cham
Sprache englisch
Maße 155 x 235 mm
Gewicht 278 g
Themenwelt Informatik Grafik / Design Digitale Bildverarbeitung
Informatik Theorie / Studium Künstliche Intelligenz / Robotik
Schlagworte Applications • Artificial Intelligence • Bioinformatics • color image processing • color images • Computer Science • computer vision • conference proceedings • Deep learning • Digital image • Education • Image Analysis • image matching • Image Processing • Image Quality • Image Segmentation • Informatics • learning • machine learning • Medical Images • Neural networks • pattern recognition • reference image • Research
ISBN-10 3-030-62468-4 / 3030624684
ISBN-13 978-3-030-62468-2 / 9783030624682
Zustand Neuware
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