Motivation: We propose a motion aware DNN model for cardiac sequence segmentation.
Goal(s): We construct an in-house dataset which has three advantages: segmentation annotations covering the cardiac cycle; comprehensive annotations, including the annotations of interventricular groove structure; fine annotations of endocardium.
Approach: We propose an edge focus loss to make the segmented boundaries be consistent with the local gradient of the input images and propose a quality control method based on Image Moments to filter abnormal predictions.
Results: The experimental results highlight the accuracy of the proposed model, and the fine segmentation results could be used to estimate accurate clinical indicators for clinical diagnosis.
Impact: In experiments, we compare the proposed model with 12 state-of-the-art segmentation models, and our model have obtained the highest accuracy for the segmentation and the highest PCC on the 17-segment model.
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