Current MR lung image segmentation has huge challenges compared to CT images, specifically in terms of low contrast, non-homogeneity. We developed a series of processing to accelerate the manual correction of reference standard lung masks by the auto-seeds region growing. Finally, we developed a 3D automatic MRI lung segmentation method using deep learning with a limited dataset (41 volumes for training and 4 volumes for validation). In the primary result, this lung segmentation archived a Dice score of 0.917±0.013. In the case of limited data, it provides us a new way for MR lung segmentation.
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