用户提示先验对全身计算机断层扫描中半自动癌症病变分割的影响
Effect of User-Prompted Priors on Semi-Automated Cancer Lesion Segmentation in Whole-Body Computed Tomography
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中文总结 AI 辅助
研究用户提示先验对全身CT半自动癌症病变分割的影响，利用多平面正交用户提示先验，经3折交叉验证和外部测试，结果显示该方法能提升分割性能，支持生成高质量体积真实数据。
中文摘要 AI 辅助
在临床肿瘤学研究中，转移性癌症常用“实体瘤疗效评价标准”（RECIST）评估，测量多达五个病变直径并跟踪治疗过程，但RECIST与总生存期相关性有限。总肿瘤体积（TTV）是更强的预测指标，但通常依赖手动分割所有病变，耗时且需专业知识。利用用户提示先验（如边界框和单切片轮廓）的半自动方法可促进生成真实分割。本文研究不同用户提示先验对全身计算机断层扫描中半自动癌症病变分割性能的影响。通过3折交叉验证和外部测试，更复杂的空间先验持续提高性能，来自三个正交平面（轴向、冠状和矢状）的轮廓先验取得最佳结果。在外部测试（n = 3865个病变）中，该方法平均Dice评分为0.882，无空间先验的基线模型平均Dice评分为0.671。这些发现表明，使用多平面正交用户提示先验可改善半自动肿瘤病变分割并支持高效生成高质量体积真实数据。
英文摘要
In clinical oncology studies, metastatic cancer is commonly evaluated using "Response Evaluation Criteria in Solid Tumors" (RECIST), in which the diameter of up to five lesions is measured and followed over the course of treatment. However, RECIST shows limited correlation with overall survival. Total tumour volume (TTV) is a stronger predictor but typically relies on manual ground-truth segmentation of all lesions, which is time-consuming and requires expert domain knowledge. Semi-automated approaches leveraging user-prompted priors, such as bounding boxes and single-slice contours, as inputs to automated segmentation methods can facilitate the generation of ground-truth segmentations. This work investigates the impact of different user-prompted priors on semi-automated cancer lesion segmentation performance in whole-body computed tomography. Across 3-fold cross-validation and external testing, more complex spatial priors consistently improved performance, with contour priors from three orthogonal planes (axial, coronal and sagittal) achieving the best results. On the external test (n=3865 lesions), this approach achieved a mean Dice score of 0.882, compared to a mean Dice score of 0.671 for the baseline model with no spatial prior. These findings suggest that the use of multi-plane orthogonal user-prompted priors can improve semi-automated tumour lesion segmentation and support efficient generation of high-quality volumetric ground-truth data.
发表机构
- Uppsala University（乌普萨拉大学）
- SciLifeLab（科学生命实验室）
- Antaros Medical（安塔罗斯医疗公司）
机构由 AI 辅助整理，请以论文原文为准。
