Hasaan Hayat1,2, Aixia Sun1,3, Hanaan Hayat2,4, Sihai Liu1,3,5, Nazanin Talebloo1,6, Cody Pinger4, Jack Owen Bishop1,7, Mithil Gudi1,2, Bennett Francis Dwan1,8, Xiaohong Ma1,3,9, Yanfeng Zhao1,3,9, Anna Moore1,3, Ping Wang10,11. 1. Precision Health Program, Michigan State University, 766 Service Road, Rm. 2020, East Lansing, MI, 48823, USA. 2. Lyman Briggs College, Michigan State University, East Lansing, MI, USA. 3. Department of Radiology, College of Human Medicine, Michigan State University, East Lansing, MI, USA. 4. Institute for Quantitative Health Science and Engineering, Department of Biomedical Engineering, Michigan State University, East Lansing, MI, USA. 5. Department of Orthopedics, Beijing Charity Hospital, Capital Medical University, Beijing, China. 6. Department of Chemistry, College of Natural Science, Michigan State University, East Lansing, MI, USA. 7. Department of Neuroscience, College of Natural Science, Michigan State University, East Lansing, MI, USA. 8. College of Natural Science, Michigan State University, East Lansing, MI, USA. 9. Department of Radiology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. 10. Precision Health Program, Michigan State University, 766 Service Road, Rm. 2020, East Lansing, MI, 48823, USA. wangpin4@msu.edu. 11. Department of Radiology, College of Human Medicine, Michigan State University, East Lansing, MI, USA. wangpin4@msu.edu.
Abstract
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.
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.
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