Radiomics analyses are being increasingly employed to investigate tissue heterogeneity present within the prostate gland. We present a method for improving the repeatability of radiomics features extracted from T2-weighted images using a deep normalization technique based on fully convolutional networks (FCNs). We test the repeatability of select radiomics features on a previously published test-retest prostate dataset. We demonstrate that the intraclass correlation coefficient of first-order statistics features extracted from images normalized using the FCN-based pre-processor is consistently higher than for features extracted from non-normalized images.
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