Development of Deep Learning algortihms for the automated Calcium Scoring identification from radiological studies
Principal Investigator
Name
Fernando Martin
Degrees
Ph.D.
Institution
WellSpan Health
Position Title
Director, Enterprise Applications
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-905
Initial CDAS Request Approval
Apr 4, 2022
Title
Development of Deep Learning algortihms for the automated Calcium Scoring identification from radiological studies
Summary
We would like to reproduce the work of Lessmann et. al. (Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions, IEEE Transactions on Medical Imaging, 2018; 37(2):615-625) as well as van Velzen et. a. (Deep Learning for Automatic Calcium Scoring in CT: Validation Using Multiple Cardiac and Chest CT Protocols, Radiology 2020; 295:66-79) as a precursor for the possible development of our own deep learning methods.
Aims
* reproduce published deep learning methods for the automatic identification and classification of calcium scoring.
* develop novel machine learning methodologies for the identification, classification and quantification of calcium scoring from low-dose spectral CTs.
Collaborators
(no collaborators at the moment)