Keywords: Heart Failure, Segmentation, Open-Source
Motivation: Cardiac magnetic resonance (CMR) image segmentation remains a time-consuming task. Deep learning (DL) segmentation models have advanced considerably in recent years, but they lack flexibility for manual adjustments.
Goal(s): We sought to develop an open-source, flexible plug-and-play inline CMR image segmentation platform.
Approach: We implemented in-house algorithms in a clinical scanner as separate Python modules, allowing new and existing DL models to be integrated into clinical workflow.
Results: We assessed the feasibility of the automated plug-and-play platform for inline cine segmentation in 308 patients referred for clinical CMR.
Impact: This platform facilitates the rapid development and evaluation of any segmentation algorithm in a transparent and reproducible fashion. An open-source, flexible, plug-and-play inline CMR segmentation platform will enable rapid testing and evaluation of new segmentation and analysis algorithms.
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.
Keywords