9 citations · 9 across the 3 of their papers we have counts for
5 papers · 1 filter
MEDPSeg: Hierarchical polymorphic multitask learning for the segmentation of ground-glass opacities, consolidation, and pulmonary structures on computed tomography
Diedre S. Carmo, Jean A. Ribeiro, Alejandro P. Comellas +4
The COVID-19 pandemic response highlighted the potential of deep learning methods in facilitating the diagnosis, prognosis and understanding of lung diseases through automated segm…
Automatic segmentation of lung findings in CT and application to Long COVID
Diedre S. Carmo, Rosarie A. Tudas, Alejandro P. Comellas +4
Automated segmentation of lung abnormalities in computed tomography is an important step for diagnosing and characterizing lung disease. In this work, we improve upon a previous me…
Single volume lung biomechanics from chest computed tomography using a mode preserving generative adversarial network
Muhammad F. A. Chaudhary, Sarah E. Gerard, Di Wang +5
Local tissue expansion of the lungs is typically derived by registering computed tomography (CT) scans acquired at multiple lung volumes. However, acquiring multiple scans incurs i…
Recursive Refinement Network for Deformable Lung Registration between Exhale and Inhale CT Scans
Xinzi He, Jia Guo, Xuzhe Zhang +9
Unsupervised learning-based medical image registration approaches have witnessed rapid development in recent years. We propose to revisit a commonly ignored while simple and well-e…
CT Image Segmentation for Inflamed and Fibrotic Lungs Using a Multi-Resolution Convolutional Neural Network
Sarah E. Gerard, Jacob Herrmann, Yi Xin +9
The purpose of this study was to develop a fully-automated segmentation algorithm, robust to various density enhancing lung abnormalities, to facilitate rapid quantitative analysis…