Abstract #1939
Machine learning and computer vision based quantification of cell number in MRI-based cell tracking
Muhammed Jamal Afridi 1 , Matt Latourette 2 , Margaret F Bennewitz 3 , Arun Ross 1 , Xiaoming Liu 1 , and Erik M Shapiro 2
1
Department of Computer Science and
Engineering, Michigan State University, East Lansing,
MI, United States,
2
Department
of Radiology, Michigan State University, East Lansing,
MI, United States,
3
Vascular
Medicine Institute, University of Pittsburgh,
Pittsburgh, PA, United States
MRI detection of single cells is an underutilized
advancement in MRI-based cell tracking. One reason for
its underutilization has been a lack of methods for
quantifying information in these images. For example,
single cell detection enables quantification of cell
numbers and accurate cell localization. To achieve
single cell detection by MRI, cells are labeled with
superparamagnetic iron oxide particles allowing their
detection as punctate hypointensities in T2*-weighted
MRI. We have developed a machine learning and computer
vision based strategy for the generalizable detection
and quantification of MRI-based single cell detection.
Our approach can detect spots with an accuracy of 99.8%.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Click here for more information on becoming a member.
