Learning Prostate Anatomy at Test Time for Cancer Detection in Micro-Ultrasound

Obed Korshie Dzikunu; Mohammad Mahdi Abootorabi; Mohamed Harmanani; Paul F. R. Wilson; Emma Willis; Ferdinand Luger; Adam Kinnaird; Brian Wodlinger; Parvin Mousavi; Purang Abolmaesumi

arXiv · 2608.20557

Learning Prostate Anatomy at Test Time for Cancer Detection in Micro-Ultrasound

Obed Korshie Dzikunu·Mohammad Mahdi Abootorabi·Mohamed Harmanani·Paul F. R. Wilson·Emma Willis·Ferdinand Luger·Adam Kinnaird·Brian Wodlinger·Parvin Mousavi·Purang Abolmaesumi

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Abstract

Domain shift across clinical centers using different imaging hardware or acquisition protocols remains a fundamental barrier to deploying deep learning models for prostate cancer (PCa) detection. Existing test-time adaptation (TTA) methods address distribution shift through entropy minimization or augmentation-based self-supervision, correcting for statistical differences in image appearance but ignoring the anatomical structure of the target domain. We propose ANT, a segmentation-guided TTA framework that adapts a pretrained cancer detection encoder to the target domain by solving an auxiliary prostate segmentation task at test time, supervised by pseudo-masks from a frozen pretrained segmentation network. By aligning encoder representations to prostate anatomy in the target domain, ANT corrects domain-specific feature drift while preserving cancer-discriminative structure. The model was trained on 693 patients imaged with an earlier-generation micro-ultrasound scanner in a multi-center clinical trial, and evaluated on 118 patients acquired with a newer-generation system across two centers in another clinical trial. Under a leave-one-center-out protocol with identical evaluation conditions across all methods, ANT improves mean AUC by 2.9% and 3.6% at the biopsy-core and patient levels, respectively, over no adaptation, outperforming TTA baselines. Code is available at: https://github.com/ObedDzik/ant.git.

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Obed Korshie Dzikunu, Mohammad Mahdi Abootorabi, Mohamed Harmanani, Paul F. R. Wilson, Emma Willis, Ferdinand Luger, Adam Kinnaird, Brian Wodlinger, Parvin Mousavi, Purang Abolmaesumi. 2026-08-20. Learning Prostate Anatomy at Test Time for Cancer Detection in Micro-Ultrasound. https://arxiv.org/abs/2608.20557

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