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Magnetic Resonance Imaging01:24

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Pediatric pancreas segmentation from MRI scans with deep learning.

Elif Keles1, Merve Yazol2, Gorkem Durak1

  • 1Department of Radiology, Northwestern University, IL, USA.

Pancreatology : Official Journal of the International Association of Pancreatology (IAP) ... [Et Al.]
|July 11, 2025
PubMed
Summary

PanSegNet, a deep learning algorithm, accurately segments pediatric pancreas MRI scans in children with pancreatitis and healthy controls. This validated tool offers expert-level performance, advancing accessible pediatric pancreatic imaging.

Keywords:
Artificial intelligenceAutomatic segmentationDeep learningPediatric pancreas MRIPediatric pancreatitis

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Pediatric Radiology

Background:

  • Pediatric pancreatic diseases require accurate imaging for diagnosis and management.
  • Manual segmentation of pancreas on MRI is time-consuming and subject to inter-observer variability.
  • Deep learning offers potential for automated and efficient image analysis.

Purpose of the Study:

  • To evaluate and validate PanSegNet, a deep learning algorithm for pediatric pancreas segmentation on MRI.
  • To assess PanSegNet's performance in children with acute pancreatitis (AP), chronic pancreatitis (CP), and healthy controls.

Main Methods:

  • Retrospective collection of 84 pediatric MRI scans (2-19 years) from healthy children and those with AP/CP.
  • Manual pancreas segmentation by pediatric radiologists, confirmed by a senior radiologist.
  • Quantitative assessment of PanSegNet segmentations using Dice Similarity Coefficient (DSC) and Hausdorff distance (HD95).

Main Results:

  • PanSegNet achieved high DSC scores: 88% (controls), 81% (AP), and 80% (CP).
  • HD95 values demonstrated good segmentation accuracy across all groups.
  • Strong agreement was observed between automated and manual pancreas volumes (R² = 0.85 controls, 0.77 diseased).

Conclusions:

  • PanSegNet is the first validated deep learning tool for pediatric pancreas MRI segmentation.
  • The algorithm demonstrates expert-level performance in segmenting pancreata in healthy and diseased pediatric populations.
  • The tool and annotated dataset are publicly available to advance research in pediatric pancreatic imaging.