Keywords: Data Processing, SegmentationDue to the inherent locality of convolutional operations, convolution neural network (CNN) often exhibits limitations in explicitly modeling long-distance dependencies. In this paper, we propose a novel hybrid multi-level graph neural (HMGN) network that combines the CNN and graph neural network to capture both local and non-local image features at multiple scales. With the proposed patch graph attention module, the HMGN network can capture image features over a large receptive field, resulting in more accurate segmentation of cardiac structures. Experiments on two public datasets show the proposed method obtains improved segmentation performance over the state-of-the-art methods.
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