Automatic Abnormality Detection and Comparison between Low Dose and Regular CT
Principal Investigator
Name
Ehsan Dehghan
Degrees
Ph.D.
Institution
IBM Research
Position Title
Research Staff Member
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-184
Initial CDAS Request Approval
Dec 10, 2015
Title
Automatic Abnormality Detection and Comparison between Low Dose and Regular CT
Summary
Automatic detection of lung abnormalities has been an active topic of research. Most of the abnormality detection algorithms were designed and trained using regular dose CT volumes. Since low-dose CT (LDCT) screening was shown to be effective in reducing the mortality due to lung cancer, the number of low-dose CT volumes is going to increase significantly. Therefore, there is a need to investigate the performance of previous abnormality detection algorithms on LDCT and develop abnormality detection algorithms for LDCT if needed.
This project will compare the performance of available lung abnormality detection algorithms on LDCT and regular CT in terms of sensitivity and specificity. New algorithms will be developed if the performance of the available algorithms on LDCT are not satisfactory for clinical use.
This project will compare the performance of available lung abnormality detection algorithms on LDCT and regular CT in terms of sensitivity and specificity. New algorithms will be developed if the performance of the available algorithms on LDCT are not satisfactory for clinical use.
Aims
1- Investigating the performance of available abnormality detection algorithms on LDCT in terms of sensitivity and specificity.
2- Develop a framework for detection of lung abnormalities in LDCT.
Collaborators
Tanveer Syeda-Mahmood, IBM Research
David Beymer. IBM Research