Purpose

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.

Procedures

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.

Results

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.

Conclusions

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.

中文翻译：

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小鼠胰岛移植磁粒子成像的人工智能分析。

目的

目前用于细胞治疗的磁粒子成像 (MPI) 量化方法因缺乏从图像背景中分割信号的可靠、标准化方法而受到阻碍。这就需要开发用于 MPI 分析的人工智能 (AI) 系统。

程序

我们利用无监督机器学习领域的规范算法（称为K-means++）来分割图像的感兴趣区域 (ROI)，并使用标准曲线模型执行铁定量分析。我们使用胰岛和小鼠模型生成体外、体内和离体数据，并应用 AI 算法来深入了解这些 MPI 数据的分割和铁预测。体外模型包括对不同数量的 VivoTrax 标记的胰岛进行成像。通过在小鼠肾囊下移植越来越多的标记胰岛来产生体内小鼠模型。离体数据是从离体肾移植物的 MPI 图像中获得的。

结果

K -means++算法以最小的噪声分割体外模型的 ROI 。观察到胰岛数量与胰岛中总铁值（TIV）的增加预测之间存在线性相关性。体内MPI扫描ROI的分割结果显示，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 进行分段后，观察到线性趋势，其中 ROI 强度的增加导致胰岛 TIV 的增加。通过类内相关系数验证对算法性能进行统计评估，我们观察到基于K-means++的模型在 MPI 数据的分割和量化分析方面具有出色的性能。

结论

我们已经证明了基于K-means++的模型能够在胰岛移植小鼠模型中提供 MPI 扫描分割和量化的标准化方法。