Thoracic Image Analysis

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. 

Inhaltsverzeichnis

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.