Keywords: Diagnosis/Prediction, Prostate
Motivation: Limited volumetric accuracy and inadequate prediction reliability challenge prostate zonal segmentation (PZS) in MRI, restricting clinical trustworthiness in diagnosis.
Goal(s): This study presents a novel model for PZS that incorporates volumetric information and robust relevance estimations, aiming to enhance diagnostic confidence.
Approach: We introduce a pseudo-3D model that leverages comprehensive attention mechanisms and a query-
context feature to achieve prediction and uncertainity aware PZS evaluated on two large MRI prostate datasets.
Results: Our model achieved state-of-the-art performance in PZS with a Dice Score of 0.92 in transitional (TZ) and 0.75 in peripheral (PZ) prostate zones. The query-based relevance improved the interpretability by 12%.
Impact: This work supports clinicians in confidently interpreting prostate imaging, potentially reducing unnecessary biopsies and facilitatingtimely intervention. It also encourages advancements in relevance-based AI diagnostics, paving the way forenhanced accuracy and interpretability across medical imaging and broader diagnostic applications.
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