Novel System for Automatically Diagnosing Pancreatic Intraductal Papillary Mucinous Neoplasms

Technology #34177

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The diagram provides an overview of the new system for automatically diagnosing IPMNs.
Ulas Bagci, Ph.D.
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Rodney Lalonde
Candice Bolan, M.D., Mayo Clinic
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Michael Wallace, M.D., Mayo Clinic
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Andrea Adkins
Assistant Director 407.823.0138
Patent Protection

US Patent Pending US 2020/0000362 A1
Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches
IEEE Transactions on Medical Imaging, vol. 38, no. 8, pp. 1777-1787, Aug. 2019
Deep multi-modal classification of intraductal papillary mucinous neoplasms (IPMN) with canonical correlation analysis
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, pp. 800-804

New diagnostic tool speeds the process of detecting and analyzing precancerous pancreatic cysts.

Researchers at the University of Central Florida and the Mayo Clinic have developed an automated system that enables radiologists to detect and assess the cancer risk of pancreatic cysts faster and more effectively than using conventional MRI technology and radiographic guidelines. The cysts, called intraductal papillary mucinous neoplasms (IPMNs), are radiographically identifiable precursors to pancreatic cancer. Consequently, the early detection and risk assessment of IPMNs is vital.

With conventional technology, a radiologist may have to read more than 1,000 images to evaluate a cyst for just one patient. The new system streamlines the process by using MRI data with machine learning strategies to improve tumor risk stratification (characterization). Also, the invention can enable non-invasive cancer staging and prognosis, and foster personalized treatment planning as a part of precision medicine.

Technical Details

The invention comprises a system and methods for automatically diagnosing IPMNs in a pancreas using multi-modal MRI data (TI and T2 images). In one example application, the system comprises an MRI scanner and a processor programmed to compute the minimum and maximum intensity projections that correspond to the Tl and T2 MRI scans of a patient’s pancreas. The intensity projections then go into a pre-trained image recognition convolutional neural network (CNN) algorithm to obtain feature vectors. Next, canonical correlation analysis (CCA)-based feature fusion is performed to produce a final vector with discriminative and transformed feature representation. Finally, the system employs a support vector machine (SVM) classifier to determine whether the pancreas includes IPMNs.


  • Improves tumor characterization in early detection and precise risk assessment of IPMNs
  • Does not require manual segmentation of a pancreas or cysts
  • Does not require explicit sample balancing


  • Biomedical imaging and radiology
  • Hospitals and health centers