Keywords: Data Analysis, Body, Deep learning, DixonAutomated image processing and organ segmentation are critical to the quantitative analysis of population-scale imaging studies. We have implemented an end-to-end pipeline for neck-to-knee Dixon MRI data based on the UK Biobank abdominal protocol. Bias-field correction, blending across series boundaries, and fat-water swap correction are performed in the preprocessing steps. A hybrid attention-convolutional neural network model segments multiple abdominal organs, major bones, along with adipose and muscle tissue. The application of neural network models, to both swap detection and segmentation, produces a computationally-efficient pipeline that scales to accommodate tens of thousands of datasets.
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