Asset Type: Posters, Imaging, Imaging Core Lab, Additional  Therapeutic Areas

AI-Driven Instance Segmentation of Cysts in Autosomal Dominant Polycystic Kidney Disease (ADPKD)

This poster evaluates AI-driven instance segmentation for automatically identifying and delineating individual kidney cysts in autosomal dominant polycystic kidney disease (ADPKD) on MRI. Researchers compared two AI architectures against radiologist-generated ground truth and found that both supported automated cyst identification, with Mask R-CNN demonstrating stronger overall performance. The findings show the potential of AI-based segmentation to enable scalable cyst burden analysis and longitudinal tracking, supporting exploration of novel imaging biomarkers in ADPKD.

Explore how AI can automate individual cyst identification and segmentation, helping address the challenges of manual annotation at scale.
Compare the performance of two AI segmentation approaches, Mask R-CNN and MedSAM3, against radiologist-generated ground truth.
See the potential for automated cyst tracking over time to support cyst burden analysis and exploration of novel imaging biomarkers in ADPKD.

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AI-Driven Instance Segmentation of Cysts in Autosomal Dominant Polycystic Kidney Disease (ADPKD)

AI-Driven Instance Segmentation of Cysts in Autosomal Dominant Polycystic Kidney Disease (ADPKD)

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