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Abstract #1393

Revolutionizing Prostate Segmentation: A Query Adaptive Pseudo 3D Attention Approach

Chetana Krishnan1, Ezinwanne Onuoha1, Alex Hung2, Kyung Hyun Sung2, and Harrison Kim3
1Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL, United States, 2Radiology, University of California Los Angeles, Los Angeles, CA, United States, 3Radiology, University of Alabama at Birmingham, Birmingham, AL, United States

Synopsis

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.

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