YOLO-PVC：用于MRI中体积肝肿瘤定位的切片式检测的2D到3D整合方法
YOLO-PVC: 2D-to-3D Consolidation of Slice-wise Detections for Volumetric Liver Tumor Localization in MRI
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中文总结 AI 辅助
针对切片式2D目标检测器在体积数据中预测碎片化不稳定的问题，提出YOLO-PVC框架，通过几何整合提升肝脏肿瘤定位的3D IoU至0.710，优于多种基线方法。
中文摘要 AI 辅助
切片式2D目标检测器因计算效率高、可扩展性强，正越来越多地应用于体积数据，但它们常沿深度轴产生碎片化且不稳定的预测。我们提出YOLO-PVC，这是一种轻量且与模型无关的框架，用于将切片式检测从2D整合到3D。该方法强制深度连续性，使用稳健的百分位数统计聚合边界框坐标，并通过轻量的基于MLP的校准模块进一步细化轴向范围。与简单的堆叠或平均策略不同，YOLO-PVC明确解决了深度维度上的漏检和异常切片问题。在涵盖三类肿瘤的3D肝脏MRI体积上进行的实验表明，其性能优于多种聚合基线。启发式PVC的总体3D IoU为0.665，而经校准的变体将性能进一步提升至0.710，且具有较高的平面重叠度（BEV IoU≈0.78）。这些结果表明，结构化几何整合为临床MRI中的体积肝肿瘤定位提供了一种有效且实用的解决方案。
英文摘要
Slice-wise 2D object detectors are increasingly applied to volumetric data due to their computational efficiency and scalability, yet they often yield fragmented and unstable predictions along the depth axis. We propose YOLO-PVC, a lightweight and model-agnostic framework for 2D-to-3D consolidation of slice-wise detections. The method enforces depth continuity, aggregates bounding box coordinates using robust percentile statistics, and further refines axial extent through a lightweight MLP-based calibration module. Unlike naïve stacking or averaging strategies, YOLO-PVC explicitly addresses missing detections and outlier slices along the depth dimension. Experiments on 3D liver MRI volumes across three tumor categories demonstrate consistent improvements over multiple aggregation baselines. The heuristic PVC achieves an overall $\mathrm{IoU}_{3D}$ of $0.665$, while the calibrated variant further improves performance to $0.710$, with high planar overlap ($\mathrm{BEV\ IoU} \approx 0.78$). These results demonstrate that structured geometric consolidation provides an effective and practical solution for volumetric liver tumor localization in clinical MRI.
发表机构
- ESME Research Lab（ESME研究实验室）
- Université Paris-Est Créteil（巴黎东部克雷泰伊大学）
- LRE EPITA（EPITA LRE实验室）
- Institute of Space Technology（空间技术研究所）
- Henri Mondor University Hospital（亨利·蒙多大学医院）
机构由 AI 辅助整理，请以论文原文为准。
