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Your search keyword '"Thiele, Christine"' showing total 2 results
Start Over Author "Thiele, Christine" Topic hyperspectral imaging
2 results on '"Thiele, Christine"'

1. Algorithm for Mapping Kidney Tissue Water Content during Normothermic Machine Perfusion Using Hyperspectral Imaging

Author

Markgraf, Wenke, Lilienthal, Jannis, Feistel, Philipp, Thiele, Christine, and Malberg, Hagen

Subjects
water content, multivariate data analysis, lcsh:T55.4-60.8, hyperspectral imaging, data preprocessing, partial least squares regression, normothermic machine perfusion, kidney tissue, lcsh:Industrial engineering. Management engineering, lcsh:Electronic computers. Computer science, lcsh:QA75.5-76.95
Abstract

The preservation of kidneys using normothermic machine perfusion (NMP) prior to transplantation has the potential for predictive evaluation of organ quality. Investigations concerning the quantitative assessment of physiological tissue parameters and their dependence on organ function lack in this context. In this study, hyperspectral imaging (HSI) in the wavelength range of 500&ndash, 995 nm was conducted for the determination of tissue water content (TWC) in kidneys. The quantitative relationship between spectral data and the reference TWC values was established by partial least squares regression (PLSR). Different preprocessing methods were applied to investigate their influence on predicting the TWC of kidneys. In the full wavelength range, the best models for absorbance and reflectance spectra provided Rp2 values of 0.968 and 0.963, as well as root-mean-square error of prediction (RMSEP) values of 2.016 and 2.155, respectively. Considering an optimal wavelength range (800&ndash, 980 nm), the best model based on reflectance spectra (Rp2 value of 0.941, RMSEP value of 3.202). Finally, the visualization of TWC distribution in all pixels of kidneys&rsquo, HSI image was implemented. The results show the feasibility of HSI for a non-invasively and accurate TWC prediction in kidneys, which could be used in the future to assess the quality of kidneys during the preservation period.

Published
2020

2. Hyperspectral Imaging during Normothermic Machine Perfusion—A Functional Classification of Ex Vivo Kidneys Based on Convolutional Neural Networks.

Author

Sommer, Florian, Sun, Bingrui, Fischer, Julian, Goldammer, Miriam, Thiele, Christine, Malberg, Hagen, and Markgraf, Wenke

Subjects
CONVOLUTIONAL neural networks, KIDNEYS, PERFUSION, KIDNEY physiology, TRANSPLANTATION of organs, tissues, etc.
Abstract

Facing an ongoing organ shortage in transplant medicine, strategies to increase the use of organs from marginal donors by objective organ assessment are being fostered. In this context, normothermic machine perfusion provides a platform for ex vivo organ evaluation during preservation. Consequently, analytical tools are emerging to determine organ quality. In this study, hyperspectral imaging (HSI) in the wavelength range of 550–995 nm was applied. Classification of 26 kidneys based on HSI was established using KidneyResNet, a convolutional neural network (CNN) based on the ResNet-18 architecture, to predict inulin clearance behavior. HSI preprocessing steps were implemented, including automated region of interest (ROI) selection, before executing the KidneyResNet algorithm. Training parameters and augmentation methods were investigated concerning their influence on the prediction. When classifying individual ROIs, the optimized KidneyResNet model achieved 84% and 62% accuracy in the validation and test set, respectively. With a majority decision on all ROIs of a kidney, the accuracy increased to 96% (validation set) and 100% (test set). These results demonstrate the feasibility of HSI in combination with KidneyResNet for non-invasive prediction of ex vivo kidney function. This knowledge of preoperative renal quality may support the organ acceptance decision. [ABSTRACT FROM AUTHOR]

Published
2022
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