Abstract #1334

Evaluation of input data and UNet based convolutional network architectures for automated muscle annotation in 2D and 3D

Martijn Froeling ¹, Lara Schlaffke², Marlena Rohm², Ivana Isgum³, Hermien E Kan⁴, and Jelmer M Wolterink³

¹Department of Radiology, University medical center utrecht, Utrecht, Netherlands, ²Department of Neurology BG, University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany, ³Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands, ⁴Dept of Radiology, C.J. Gorter Center for High Field MRI, Leiden University Medical Center, Leiden, Netherlands

Manual annotation of muscle is still one of the most time-consuming steps in skeletal muscle MRI research. In this study we have investigated three aspects of automated muscle annotation using deep convolutional networks. First, we directly compare five different network architectures. Second, we compare the effect of providing various input data all based on Dixon imaging. Third, we investigate the effect of the amount of training data provided to the network. In summary we found that UNet-like convolutional networks allow for accurate and precise annotation of calf muscle in 2D and 3D and that the data provided is the strongest predictor of success.

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.