Abstract #5114

TraceOrg: An AI Platform for Reproducible ADPKD Organ/Cyst Contouring on MRI

Xinzi He¹, Qing Xiong², Chenglin Zhu³, Zhongxiu Hu⁴, Yelynn Kim⁴, Hreedi Dev³, Kurt Teichman³, Anna Caroli⁵, Elisa Scalco⁶, Giulia Villa⁷, Francesca Lussana⁸, Siria Pasini⁵, Mert R. Sabuncu⁴, and Martin R. Prince³

¹Cornell University, New York, NY, United States, ²Radiology, Weill Cornell Medicine, New York City, NY, United States, ³Radiology, Weill Cornell Medicine, New York, NY, United States, ⁴Weill Cornell Medicine, New York, NY, United States, ⁵Bioengineering Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Bergamo, Italy, ⁶Institute of Biomedical Technologies, National Research Council (ITB-CNR), Bergamo, Italy, ⁷Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Bergamo, Italy, ⁸Department of Management, Information and Production Engineering, University of Bergamo, Dalmine, Italy

Synopsis

Keywords: AI/ML Software, Kidney

Motivation: Abdominal organ volumes are critical MRI biomarkers in many diseases, including autosomal dominant polycystic kidney disease.

Goal(s): To develop an internet accessible, kidney/liver/cyst volume measurement tool.

Approach: 3D Unet transformer hybrid model internet based calculator for kidney liver and renal cysts uploads DICOM MRimages with automatic anonymization performed in the browser software to ensure HIPAA compliance. External validation: 153 MRIs from another country (coronal T2 fatsat, and T1 breathhold sequences).

Results: TraceOrg demonstrated high Dice, low Average Symmetric Surface Distance, low Hausdorff Distance for kidney, liver and renal cyst segmentation compared to manual expert ground truth segmentation.

Impact: TraceOrg provides accurate and efficient segmentations for measuring kidney liver and renal cyst volumes to enhance radiologist subjective assessments of ADPKD abdominal MRI and is readily accessible via internet.

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