Thoracic Image Analysis

Second International Workshop, TIA 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings
Herausgegeben:Petersen, Jens; San José Estépar, Raúl; Schmidt-Richberg, Alexander; Gerard, Sarah; Lassen-Schmidt, Bianca; Jacobs, Colin; Beichel, Reinhard; Mori, Kensaku

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Lecture Notes in Computer Science 12502
• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-030-62468-2
• 1st ed. 2020
• Seitenzahl: 176
• Erscheinungstermin: 4. November 2020
• Englisch
• Abmessung: 235mm x 155mm x 10mm
• Gewicht: 277g
• ISBN-13: 9783030624682
• ISBN-10: 3030624684
• Artikelnr.: 60164925

Inhaltsangabe

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.