深度学习模型PanSegNet实现儿童胰腺精准分割，助力无辐射影像诊断。
Pediatric Pancreas Segmentation from MRI Scans with Deep Learning
- 基于深度学习的PanSegNet自动分割儿童胰腺，支持健康与病变状态。
- 在健康、急性及慢性胰腺炎患儿中分别达到88%、81%、80%的分割准确率。
- 结果可媲美专家水平，适合儿科影像研究与临床辅助诊断使用。
本研究旨在评估并验证一种名为PanSegNet的深度学习算法，用于儿童急性胰腺炎（AP）、慢性胰腺炎（CP）及健康对照组的MRI胰腺分割。在伦理审批下，我们回顾性收集了2015-2024年于加齐大学获得的84例儿童MRI扫描（1.5T/3T Siemens Aera/Verio），年龄2-19岁。数据集包含健康儿童及根据临床标准确诊的AP或CP患者。由儿科和普通放射科医生手动勾画胰腺，经资深儿科放射科医生确认。采用骰子相似系数（DSC）和95%分位数豪斯多夫距离（HD95）评估算法性能，克朗巴赫kappa值衡量观察者间一致性。结果显示：42例AP/CP患儿（平均年龄11.73±3.9岁）与42例健康儿童（平均年龄11.19±4.88岁）的T2加权MRI扫描被分析。PanSegNet在健康组、急性胰腺炎组、慢性胰腺炎组分别取得88%、81%、80%的DSC，HD95分别为3.98毫米、9.85毫米、15.67毫米。观察者间一致性kappa值为0.86（健康组）、0.82（胰腺炎组），组内一致性达0.88和0.81。自动化体积与人工标注高度一致（健康组R²=0.85，疾病组R²=0.77），证明其临床可靠性。结论：PanSegNet是首个经过验证的儿童胰腺MRI分割深度学习工具，在健康与疾病状态下均达到专家级表现。该算法及标注数据集已公开于GitHub和OSF，推动无辐射、可及的儿科胰腺影像研究发展。
原文摘要 · Abstract (English)
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.
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