Meeting Banner
Abstract #4356

Brain tumor segmentation using out-of-distribution feature from normal MR images

Namho Jeong1, Beomgu Kang1, and Hyunwook Park1
1Korea Advanced Institute of Science and Technology, Daejeon, Korea, Republic of

Synopsis

Keywords: Machine Learning/Artificial Intelligence, TumorAbundant data of healthy subjects is available and thus it would be helpful if normal data can boost the brain tumor segmentation. We proposed the out-of-distribution (OOD) feature based on the learned distribution of normal data to discriminate abnormal tumors. Once the neural network was trained to synthesize other contrast MR images with normal data, it failed to properly synthesize the tumor tissues. This OOD characteristic was used for the generation of new feature that can help the tumor segmentation. It was verified that the OOD features contributed to an improvement of segmentation through experiments.

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.

Keywords