Keywords: Kidney, Kidney, Segmentation, TKV, Transformers
Motivation: Convolutional Neural Networks (CNNs) have long been the go-to deep-learning architecture for medical image segmentation, but in recent years transformer-based architectures adapted from large language models are setting a new standard.
Goal(s): The aim of this study was to test if transformers are suitable for 3D kidney segmentation on high-resolution MRI.
Approach: A transformer-based deep-learning architecture (UNETR) was trained and tested against a supervised method on 82 patient datasets from the iBEAt study on diabetic kidney disease.
Results: UNETR provides fast segmentation with comparable results to the supervised method, but additional refinement is needed to reduce the limits of agreement.
Impact: Novel transformer-based architectures for medical image segmentation may be useful for fast 3D segmentation of individual kidneys.
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