通过检测针尖与针柄实现骨盆植入术中多针精确定位。
Multi-needle Localization for Pelvic Seed Implant Brachytherapy based on Tip-handle Detection and Matching
- 将定位问题转为针尖针柄检测与匹配，提升抗干扰能力。
- 在100例患者数据上精度和F1值优于基于分割的方法。
- 适合复杂临床场景下需要高精度针位重建的医生使用。
术中CT图像中多针精确定位对优化骨盆种子植入近距离放射治疗至关重要，但受图像对比度差和针体粘连影响，难度大。本文提出一种新方法，将针定位重构为针尖-针柄检测与匹配问题。基于HRNet设计无锚框网络，通过解耦分支分别预测热图与极角，实现多尺度特征提取及针尖、针柄中心与方向的精准检测。为关联检测到的针尖与针柄形成完整针体，提出贪心匹配与合并（GMM）算法，用于解决带约束的不平衡分配问题（UAP-C），通过距离度量迭代选择最可能的配对并合并以重建三维针路径。在100例患者数据集上评估，该方法在精度和F1分数上均优于采用nnUNet的分割方法，展现出更强鲁棒性与准确性，适用于复杂临床环境下的针位定位。
原文摘要 · Abstract (English)
Accurate multi-needle localization in intraoperative CT images is crucial for optimizing seed placement in pelvic seed implant brachytherapy. However, this task is challenging due to poor image contrast and needle adhesion. This paper presents a novel approach that reframes needle localization as a tip-handle detection and matching problem to overcome these difficulties. An anchor-free network, based on HRNet, is proposed to extract multi-scale features and accurately detect needle tips and handles by predicting their centers and orientations using decoupled branches for heatmap regression and polar angle prediction. To associate detected tips and handles into individual needles, a greedy matching and merging (GMM) method designed to solve the unbalanced assignment problem with constraints (UAP-C) is presented. The GMM method iteratively selects the most probable tip-handle pairs and merges them based on a distance metric to reconstruct 3D needle paths. Evaluated on a dataset of 100 patients, the proposed method demonstrates superior performance, achieving higher precision and F1 score compared to a segmentation-based method utilizing the nnUNet model,thereby offering a more robust and accurate solution for needle localization in complex clinical scenarios.
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