基于CT的深度学习模型自动勾画儿童腹腔关键器官，提升放疗规划效率。
Deep learning-based auto-contouring of organs/structures-at-risk for pediatric upper abdominal radiotherapy
- 融合本地与公开数据训练多器官分割模型，提升泛化能力。
- 多数器官分割Dice系数超0.9，儿童0-2岁组性能略低。
- 临床评估认可可用性，适合多种肿瘤类型和影像条件。
本研究旨在开发一种基于CT的多器官分割模型，用于儿科上腹部肿瘤中关键器官的自动勾画，并评估其在多个数据集上的鲁棒性。使用189例肾肿瘤和神经母细胞瘤患儿的术后CT（自建数据）及189例公开胸部腹部CT数据集。共勾画17个器官：9个由临床医生标注（类型1），8个由TotalSegmentator生成（类型2）。分别用自建数据（Model-PMC-UMCU）和联合数据（Model-Combined）训练模型。性能通过骰子相似系数（DSC）、95%豪斯多夫距离（HD95）和平均表面距离（MSD）评估。两名临床医生对15例患者的分割结果进行5级量表评分。结果显示，Model-PMC-UMCU在9个类型1器官中，5个的平均DSC高于0.95，脾脏和心脏介于0.90至0.95之间，胃肠道与胰腺低于0.90。Model-Combined在两个数据集上表现更稳健。临床评估显示良好可用性，两位医生对6个类型1器官评分高于4分，6个类型2器官高于3分。仅在年龄组间存在显著差异，尤其在左肺和胰腺，0-2岁组表现最差。结论：该多器官分割模型经联合数据训练后具备更强鲁棒性，适用于多种器官及临床场景。
原文摘要 · Abstract (English)
Purposes: This study aimed to develop a computed tomography (CT)-based multi-organ segmentation model for delineating organs-at-risk (OARs) in pediatric upper abdominal tumors and evaluate its robustness across multiple datasets. Materials and methods: In-house postoperative CTs from pediatric patients with renal tumors and neuroblastoma (n=189) and a public dataset (n=189) with CTs covering thoracoabdominal regions were used. Seventeen OARs were delineated: nine by clinicians (Type 1) and eight using TotalSegmentator (Type 2). Auto-segmentation models were trained using in-house (ModelPMC-UMCU) and a combined dataset of public data (Model-Combined). Performance was assessed with Dice Similarity Coefficient (DSC), 95% Hausdorff Distance (HD95), and mean surface distance (MSD). Two clinicians rated clinical acceptability on a 5-point Likert scale across 15 patient contours. Model robustness was evaluated against sex, age, intravenous contrast, and tumor type. Results: Model-PMC-UMCU achieved mean DSC values above 0.95 for five of nine OARs, while spleen and heart ranged between 0.90 and 0.95. The stomach-bowel and pancreas exhibited DSC values below 0.90. Model-Combined demonstrated improved robustness across both datasets. Clinical evaluation revealed good usability, with both clinicians rating six of nine Type 1 OARs above four and six of eight Type 2 OARs above three. Significant performance 2 differences were only found across age groups in both datasets, specifically in the left lung and pancreas. The 0-2 age group showed the lowest performance. Conclusion: A multi-organ segmentation model was developed, showcasing enhanced robustness when trained on combined datasets. This model is suitable for various OARs and can be applied to multiple datasets in clinical settings.
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