Precision Health Program, Michigan State University, 766 Service Road, Rm. 2020, East Lansing, MI, 48823, USA.
Lyman Briggs College, Michigan State University, East Lansing, MI, USA.
Mol Imaging Biol. 2021 Feb;23(1):18-29. doi: 10.1007/s11307-020-01533-5. Epub 2020 Aug 24.
Current approaches to quantification of magnetic particle imaging (MPI) for cell-based therapy are thwarted by the lack of reliable, standardized methods of segmenting the signal from background in images. This calls for the development of artificial intelligence (AI) systems for MPI analysis.
We utilize a canonical algorithm in the domain of unsupervised machine learning, known as K-means++, to segment the regions of interest (ROI) of images and perform iron quantification analysis using a standard curve model. We generated in vitro, in vivo, and ex vivo data using islets and mouse models and applied the AI algorithm to gain insight into segmentation and iron prediction on these MPI data. In vitro models included imaging the VivoTrax-labeled islets in varying numbers. In vivo mouse models were generated through transplantation of increasing numbers of the labeled islets under the kidney capsule of mice. Ex vivo data were obtained from the MPI images of excised kidney grafts.
The K-means++ algorithms segmented the ROI of in vitro phantoms with minimal noise. A linear correlation between the islet numbers and the increasing prediction of total iron value (TIV) in the islets was observed. Segmentation results of the ROI of the in vivo MPI scans showed that with increasing number of transplanted islets, the signal intensity increased with linear trend. Upon segmenting the ROI of ex vivo data, a linear trend was observed in which increasing intensity of the ROI yielded increasing TIV of the islets. Through statistical evaluation of the algorithm performance via intraclass correlation coefficient validation, we observed excellent performance of K-means++-based model on segmentation and quantification analysis of MPI data.
We have demonstrated the ability of the K-means++-based model to provide a standardized method of segmentation and quantification of MPI scans in an islet transplantation mouse model.
目前用于细胞治疗的磁粒子成像（MPI）定量的方法受到缺乏可靠、标准化的图像背景信号分割方法的阻碍。这就需要开发用于 MPI 分析的人工智能（AI）系统。
我们利用无监督机器学习领域中的一种标准算法，即 K-means++，对图像的感兴趣区域（ROI）进行分割，并使用标准曲线模型进行铁定量分析。我们使用胰岛和小鼠模型生成了体外、体内和离体数据，并应用 AI 算法深入了解这些 MPI 数据的分割和铁预测。体外模型包括以不同数量成像 VivoTrax 标记的胰岛。体内小鼠模型通过在小鼠肾包膜下移植越来越多标记的胰岛来生成。离体数据来自切除的肾移植 MPI 图像。
K-means++算法最小化噪声地分割了体外模型的 ROI。观察到胰岛数量与胰岛总铁值（TIV）的递增预测之间存在线性相关性。对体内 MPI 扫描的 ROI 进行分割的结果表明，随着移植胰岛数量的增加，信号强度呈线性趋势增加。对离体数据的 ROI 进行分割后，观察到线性趋势，即 ROI 强度增加导致胰岛的 TIV 增加。通过内类相关系数验证对算法性能的统计评估，我们观察到 K-means++ 基于模型的算法在胰岛移植小鼠模型的 MPI 扫描分割和定量分析方面具有出色的性能。
我们已经证明了基于 K-means++的模型能够为胰岛移植小鼠模型中的 MPI 扫描提供标准化的分割和定量方法。
