In this work, we propose a novel probabilistic reference-region-based segmentation method to automatically distinguish various pathological tissue regions within soft tissue sarcoma, including high cellularity, high T2 and necrosis. The classification is based on a calculation of the probability that a tumour voxel belongs to a given class using the quantitative diffusion and T2 information when compared to a reference tissue. The probabilistic approach provides a more realistic classification of the complex tumour microenvironment compared to the previous proposed binary classification method.
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