Keywords: Diagnosis/Prediction, Data Analysis
Motivation: The segmentation of nasopharyngeal carcinoma (NPC) is vital for diagnostic and prognostic processes. NPC segmentation is challenging due to its intricate anatomy, variability, and closeness to essential structures.
Goal(s): This study aims to improve NPC segmentation accuracy by leveraging multiple modalities, such as DCE-MRI and PET-CT. However, it is worth noting that previous research has not fully harnessed the potential of cross-modal features through cross-attention mechanisms.
Approach: This paper introduces a new approach, integrating cross-attention with the Vision Transformer structure, enabling efficient interaction between features from various modalities.
Results: The experiments show that the proposed model offers superior performance and state-of-the-art results.
Impact: The proposed method aims to enhance NPC segmentation results by utilizing multimodal medical imaging fusion, such as DCE-MRI fusion and PET-CT fusion. This approach has the potential to benefit other segmentation tasks involving multimodal medical image data.
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