The RSNA has bestowed its 2025 Alexander R. Margulis Award on a study showing that an open-source AI model can automatically segment anatomic structures on MR images independent of sequence.
Led by researchers at the University Hospital Basel in Switzerland, the study described the development and testing of TotalSegmentator MRI, a model based on a self-configuring framework called nnU-Net, a type of AI model designed to automatically recognize and outline structures in images.
The researchers trained the model to analyze age-related changes in organ volumes on a diverse set of major anatomic structures using 616 MRI and 527 CT images and then tested it on a large internal dataset of 8,672 abdominal MRI scans. They observed expected patterns, such as declining kidney, liver, and spleen volumes with age, and increasing adrenal gland volumes.
The study was published on February 18 in Radiology. Lead author Tugba Akinci D’Antonoli, MD, will accept the award at the upcoming RSNA meeting.
![A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
