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Pediatric Pancreas Segmentation from MRI Scans with Deep Learning
Journal article   Peer reviewed

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, …
Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.], Vol.25(5), pp.648-657
08/2025
DOI: 10.1016/j.pan.2025.06.006
PMID: 40645819
url
https://arxiv.org/pdf/2506.15908View
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 (R2 = 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.
 artificial intelligence automatic segmentation deep learning pediatric pancreas MRI pediatric pancreatitis

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