Keywords: Analysis/Processing, Segmentation
Motivation: Vision Transformers (ViTs) have the potentiality to outperform convolutional neural networks (CNNs) in the task of deep gray matter nuclei segmentation. However, Transformer-based models require large labeled datasets for training.
Goal(s): Our goal is to design a Transformer-based model and alleviate the model's dependence on labeled data.
Approach: We present a CNN-Transformer hybrid Network (CTNet) for deep gray matter nuclei segmentation. Moreover, a novel self-supervised pre-training approach that combines rotation prediction and masked feature reconstruction is proposed to pre-train the CTNet.
Results: Our method achieves better performance than other comparison models on human brain MRI datasets.
Impact: This is the first study that utilizes self-supervised learning methods for deep gray matter nuclei segmentation. Our method can achieve outstanding segmentation performance and effectively assist clinical doctors in the diagnosis and treatment of neurodegenerative diseases.
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