PDF(Pubmed)
Abstract：
High-resolution intraoperative PET/CT specimen imaging, coupled with prostate-specific membrane antigen (PSMA) molecular targeting, holds great potential for the rapid ex vivo identification of disease localizations in high-risk prostate cancer patients undergoing surgery. However, the accurate analysis of radiotracer uptake would require time-consuming manual volumetric segmentation of 3D images. The aim of this study was to test the feasibility of using machine learning to perform automatic nodal segmentation of intraoperative 68Ga-PSMA-11 PET/CT specimen images. Six (n = 6) lymph-nodal specimens were imaged in the operating room after an e.v. injection of 2.1 MBq/kg of 68Ga-PSMA-11. A machine learning-based approach for automatic lymph-nodal segmentation was developed using only open-source Python libraries (Scikit-learn, SciPy, Scikit-image). The implementation of a k-means clustering algorithm (n = 3 clusters) allowed to identify lymph-nodal structures by leveraging differences in tissue density. Refinement of the segmentation masks was performed using morphological operations and 2D/3D-features filtering. Compared to manual segmentation (ITK-SNAP v4.0.1), the automatic segmentation model showed promising results in terms of weighted average precision (97-99%), recall (68-81%), Dice coefficient (80-88%) and Jaccard index (67-79%). Finally, the ML-based segmentation masks allowed to automatically compute semi-quantitative PET metrics (i.e., SUVmax), thus holding promise for facilitating the semi-quantitative analysis of PET/CT images in the operating room.
摘要：
高分辨率术中PET/CT标本成像，结合前列腺特异性膜抗原（PSMA）分子靶向，在接受手术的高危前列腺癌患者中，具有快速离体识别疾病定位的巨大潜力。然而,放射性示踪剂摄取的准确分析将需要耗时的3D图像的手动体积分割。这项研究的目的是测试使用机器学习对术中68Ga-PSMA-11PET/CT标本图像进行自动节点分割的可行性。在e.v.注射2.1MBq/kg的68Ga-PSMA-11之后,在手术室中对六个(n=6)淋巴结标本进行成像。仅使用开源Python库（Scikit-learn，SciPy,Scikit-image）。k均值聚类算法（n=3个聚类）的实现允许通过利用组织密度的差异来识别淋巴结结构。使用形态学操作和2D/3D特征过滤来进行分割掩模的细化。与手动分割(ITK-SNAPv4.0.1)相比，自动分割模型在加权平均精度(97-99%)方面显示了有希望的结果，召回（68-81%），骰子系数(80-88%)和Jaccard指数(67-79%)。最后,基于ML的分割掩模允许自动计算半定量PET度量(即，SUVmax),因此有望促进手术室PET/CT图像的半定量分析。
