Pediatric Pancreas Segmentation from MRI Scans with Deep Learning

Elif Keles; Merve Yazol; Gorkem Durak; Ziliang Hong; Halil Ertugrul Aktas; Zheyuan Zhang; Linkai Peng; Onkar Susladkar; Necati Guzelyel; Oznur Leman Boyunaga; Cemal Yazici; Mark Lowe; Aliye Uc; Ulas Bagci

doi:10.48550/arxiv.2506.15908

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Pediatric Pancreas Segmentation from MRI Scans with Deep Learning

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Pediatric Pancreas Segmentation from MRI Scans with Deep Learning

Elif Keles, Merve Yazol, Gorkem Durak, Ziliang Hong, Halil Ertugrul Aktas, Zheyuan Zhang, Linkai Peng, Onkar Susladkar, Necati Guzelyel, Oznur Leman Boyunaga, …

ArXiV.org

Cornell University

06/18/2025

DOI: 10.48550/arxiv.2506.15908

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Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

Objective: Our study aimed to evaluate and validate PanSegNet, a deep learning (DL) algorithm for pediatric pancreas segmentation on MRI in children with acute pancreatitis (AP), chronic pancreatitis (CP), and healthy controls. Methods: With IRB approval, we retrospectively collected 84 MRI scans (1.5T/3T Siemens Aera/Verio) from children aged 2-19 years at Gazi University (2015-2024). The dataset includes healthy children as well as patients diagnosed with AP or CP based on clinical criteria. Pediatric and general radiologists manually segmented the pancreas, then confirmed by a senior pediatric radiologist. PanSegNet-generated segmentations were assessed using Dice Similarity Coefficient (DSC) and 95th percentile Hausdorff distance (HD95). Cohen's kappa measured observer agreement. Results: Pancreas MRI T2W scans were obtained from 42 children with AP/CP (mean age: 11.73 +/- 3.9 years) and 42 healthy children (mean age: 11.19 +/- 4.88 years). PanSegNet achieved DSC scores of 88% (controls), 81% (AP), and 80% (CP), with HD95 values of 3.98 mm (controls), 9.85 mm (AP), and 15.67 mm (CP). Inter-observer kappa was 0.86 (controls), 0.82 (pancreatitis), and intra-observer agreement reached 0.88 and 0.81. Strong agreement was observed between automated and manual volumes (R^2 = 0.85 in controls, 0.77 in diseased), demonstrating clinical reliability. Conclusion: PanSegNet represents the first validated deep learning solution for pancreatic MRI segmentation, achieving expert-level performance across healthy and diseased states. This tool, algorithm, along with our annotated dataset, are freely available on GitHub and OSF, advancing accessible, radiation-free pediatric pancreatic imaging and fostering collaborative research in this underserved domain.

Computer Science - Computer Vision and Pattern Recognition

Computer Science - Learning

Details

Title: Subtitle: Pediatric Pancreas Segmentation from MRI Scans with Deep Learning
Creators: Elif Keles - Northwestern University
Merve Yazol - Gazi University
Gorkem Durak - Northwestern University
Ziliang Hong - Northwestern University
Halil Ertugrul Aktas - Northwestern University
Zheyuan Zhang - Northwestern University
Linkai Peng - Northwestern University
Onkar Susladkar - Northwestern University
Necati Guzelyel - Northwestern University
Oznur Leman Boyunaga - Gazi University
Cemal Yazici - University of Illinois Chicago
Mark Lowe - Washington University in St. Louis School of Medicine
Aliye Uc - University of Iowa
Ulas Bagci - Northwestern University
Resource Type: Preprint
Publication Details: ArXiV.org
DOI: 10.48550/arxiv.2506.15908
ISSN: 2331-8422
Publisher: Cornell University; Ithaca, New York
Language: English
Date posted: 06/18/2025
Academic Unit: Stead Family Department of Pediatrics; Radiation Oncology; Gastroenterology, Hepatology, Pancreatology, and Nutrition
Record Identifier: 9984833484202771

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