Keywords: Machine Learning/Artificial Intelligence, Visualization, Adaptive convolution, visual pathway segmentation, deep learning
Motivation: Accurate visual pathway (VP) segmentation is critical for clinical diagnosis and surgical planning. Current deep learning-based methods struggle to capture significant context information, impacting the segmentation precision.
Goal(s): Improve multi-parametric MRI-based VP segmentation by designing an Adaptive Segmentation Network (ASN).
Approach: ASN uses adaptive convolution (AC) to dynamically adjust the kernel based on complementary context, facilitating the integration of contextual information. A spatial attention block selectively extracts relevant regions‘ features in each MRI sequence and fuses them.
Results: ASN's effectiveness is validated by segmenting the VP in MR images from two MRI sequences. It surpasses state-of-the-art techniques in VP segmentation.
Impact: The introduction of ASN, a new multi-parametric MR images segmentation approach, demonstrates superior performance in visual pathway (VP) segmentation in MR images, surpassing existing state-of-the-art techniques. This novel method effectively incorporates context information, leading to improved segmentation performance.
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