标题： 基于深度学习的腹部CT嗜铬细胞瘤分割中的解剖先验研究 显示英文标题

标题： A Study of Anatomical Priors for Deep Learning-Based Segmentation of Pheochromocytoma in Abdominal CT

摘要： 准确分割腹部CT扫描中的嗜铬细胞瘤（PCC）对于肿瘤负荷评估、预后和治疗计划至关重要。 它还可能有助于推断基因簇，减少对昂贵检测的依赖。 本研究系统评估了解剖先验知识，以确定能够提高基于深度学习的PCC分割性能的配置。 我们采用了nnU-Net框架来评估十一种标注策略，以实现嗜铬细胞瘤的准确3D分割，并引入了一套基于器官特异性解剖先验的新型多类方案。 这些先验知识来源于通常包围肾上腺肿瘤的相邻器官（例如肝脏、脾脏、肾脏、主动脉、肾上腺和胰腺），并与之前工作中使用的广泛身体区域先验进行了比较。 该框架在NIH临床中心的91名患者的105例增强CT扫描上进行训练和测试。 性能通过Dice相似性系数（DSC）、归一化表面距离（NSD）和实例级F1分数进行测量。 在所有策略中，肿瘤+肾脏+主动脉（TKA）标注实现了最高的分割精度，在DSC（p = 0.0097）、NSD（p = 0.0110）和F1分数（在IoU阈值为0.5时提高了25.84%）方面显著优于之前使用的肿瘤+身体（TB）标注，在70-30的训练测试划分上进行测量。 TKA模型在肿瘤负荷量化方面也表现出色（R^2 = 0.968），并且在所有基因亚型中都表现出强大的分割能力。 在五折交叉验证中，TKA在IoU阈值（0.1到0.5）范围内始终优于TB，进一步证明了其稳健性和泛化能力。 这些发现强调了在深度学习模型中整合相关解剖背景的价值，以实现精确的PCC分割，支持临床评估和长期监测。 显示英文摘要

摘要： Accurate segmentation of pheochromocytoma (PCC) in abdominal CT scans is essential for tumor burden estimation, prognosis, and treatment planning. It may also help infer genetic clusters, reducing reliance on expensive testing. This study systematically evaluates anatomical priors to identify configurations that improve deep learning-based PCC segmentation. We employed the nnU-Net framework to evaluate eleven annotation strategies for accurate 3D segmentation of pheochromocytoma, introducing a set of novel multi-class schemes based on organ-specific anatomical priors. These priors were derived from adjacent organs commonly surrounding adrenal tumors (e.g., liver, spleen, kidney, aorta, adrenal gland, and pancreas), and were compared against a broad body-region prior used in previous work. The framework was trained and tested on 105 contrast-enhanced CT scans from 91 patients at the NIH Clinical Center. Performance was measured using Dice Similarity Coefficient (DSC), Normalized Surface Distance (NSD), and instance-wise F1 score. Among all strategies, the Tumor + Kidney + Aorta (TKA) annotation achieved the highest segmentation accuracy, significantly outperforming the previously used Tumor + Body (TB) annotation across DSC (p = 0.0097), NSD (p = 0.0110), and F1 score (25.84% improvement at an IoU threshold of 0.5), measured on a 70-30 train-test split. The TKA model also showed superior tumor burden quantification (R^2 = 0.968) and strong segmentation across all genetic subtypes. In five-fold cross-validation, TKA consistently outperformed TB across IoU thresholds (0.1 to 0.5), reinforcing its robustness and generalizability. These findings highlight the value of incorporating relevant anatomical context in deep learning models to achieve precise PCC segmentation, supporting clinical assessment and longitudinal monitoring.