K-Nearest Neighbor Based Locally Connected Network for Fast Morphological Reconstruction in Fluorescence Molecular Tomography

Meng, Hui; Gao, Yuan; Yang, Xin; Wang, Kun; Tian, Jie

doi:10.1109/tmi.2020.2984557

Cited by 46 publications

(13 citation statements)

References 36 publications

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“…Recently, lower bounds of the Gromov-Wasserstein distance (see Mémoli [37]) have received some attention in applications [22] and in the investigation of their discriminating properties and their statistical behavior [38,55]. Furthermore, it is noteworthy that nearest neighbor distributions are of great interest in various fields in biology [39,57] as well as in physics [4,30,50]. In these fields it is quite common to consider the (mean of the) distribution of all nearest neighbors for data analysis.…”

Section: Related Workmentioning

confidence: 99%

From Small Scales to Large Scales: Distance-to-Measure Density based Geometric Analysis of Complex Data

Proksch¹,

Weitkamp²,

Staudt³

et al. 2022

Preprint

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How can we tell complex point clouds with different small scale characteristics apart, while disregarding global features? Can we find a suitable transformation of such data in a way that allows to discriminate between differences in this sense with statistical guarantees?In this paper, we consider the analysis and classification of complex point clouds as they are obtained, e.g., via single molecule localization microscopy. We focus on the task of identifying differences between noisy point clouds based on small scale characteristics, while disregarding large scale information such as overall size. We propose an approach based on a transformation of the data via the so-called Distance-to-Measure (DTM) function, a transformation which is based on the average of nearest neighbor distances. For each data set, we estimate the probability density of average local distances of all data points and use the estimated densities for classification. While the applicability is immediate and the practical performance of the proposed methodology is very good, the theoretical study of the density estimators is quite challenging, as they are based on non-i.i.d. observations that have been obtained via a complicated transformation. In fact, the transformed data are stochastically dependent in a non-local way that is not captured by commonly considered dependence measures. Nonetheless, we show that the asymptotic behaviour of the density estimator is driven by a kernel density estimator of certain i.i.d. random variables by using theoretical properties of U-statistics, which allows to handle the dependencies via a Hoeffding decomposition. We show via a numerical study and in an application to simulated single molecule localization microscopy data of chromatin fibers that unsupervised classification tasks based on estimated DTM-densities achieve excellent separation results.

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Section: Related Workmentioning

confidence: 99%

From Small Scales to Large Scales: Distance-to-Measure Density based Geometric Analysis of Complex Data

Proksch¹,

Weitkamp²,

Staudt³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Finally, by calibrating the spectrum at known temperatures, quantum dots or rare-earth fluorophores can provide spatially resolved temperature measurements with minimal instrumentation. However, traditional curve-fitting techniques to perform these calibrations often result in temperature resolutions near 1 K in fluorescent microscopy [9,10] or tomography [11,12]. Specific instances of fluorescent thermometry use include a nanoreactor where a difference of 200 K was observed as well as spatial inhomogeneities via rare-earth luminescence thermometry [13] when measuring from the center to the edge of a 300 µm nanoreactor, but the accuracy was not provided.…”

Section: Temperature Measurementsmentioning

confidence: 99%

Demonstration of Neural Networks to Reconstruct Temperatures from Simulated Fluorescent Data towards use in Bio-Microfluidics

Kullberg

Colton

Gregory

et al. 2022

Preprint

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Biological systems often have a narrow temperature range of operation, which require highly accurate spatially resolved temperature measurements, often near ±0.1K. However, many temperature sensors cannot meet both accuracy and spatial distribution requirements, often because their accuracy is limited by data fitting and temperature reconstruction models. Machine learning algorithms have the potential to meet this need, but their usage in generating spatial distributions of temperature is severely lacking in the literature. This work presents the first instance of using neural networks to process fluorescent images to map the spatial distribution of temperature. Three standard network architectures were investigated using non-spatially resolved fluorescent thermometry (simply-connected feed-forward network) or during image or pixel identification (U-net and convolutional neural network, CNN). Simulated fluorescent images based on experimental data were generated based on known temperature distributions where Gaussian white noise with a standard deviation of ±0.1 K was added. The poor results from these standard networks motivated the creation of what is termed a moving CNN, with an RMSE error of ±0.23 K, where the elements of the matrix represent the neighboring pixels. Finally, the performance of this MCNN is investigated when trained and applied to three distinctive temperature distributions characteristic within microfluidic devices, where the fluorescent image is simulated at either three or five different wavelengths. The results demonstrate that having a minimum of 103.5 data points per temperature and the broadest range of temperatures during training provides temperature predictions nearest to the true temperatures of the images, with a minimum RMSE of ±0.15 K. When compared to traditional curve fitting techniques, this work demonstrates that greater accuracy when spatially mapping temperature from fluorescent images can be achieved when using convolutional neural networks.

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“…To justify the utility of the proposed method, simulations and in vivo experiments are carried out for BLT reconstruction with 1DCNN in comparison with the IPS method. We use two metrics, the location error (LE) and the Dice index (28), to quantitative evaluate the location accuracy and the morphological similarity, respectively. The LE is the Euclidean distance between the barycenter of the reconstructed source and that of the true anomaly.…”

Section: Evaluation Indexmentioning

confidence: 99%

Bioluminescence Tomography Based on One-Dimensional Convolutional Neural Networks

Dai

et al. 2021

Front. Oncol.

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Bioluminescent tomography (BLT) has increasingly important applications in preclinical studies. However, the simplified photon propagation model and the inherent ill-posedness of the inverse problem limit the quality of BLT reconstruction. In order to improve the reconstruction accuracy of positioning and reconstruction efficiency, this paper presents a deep-learning optical reconstruction method based on one-dimensional convolutional neural networks (1DCNN). The nonlinear mapping relationship between the surface photon flux density and the distribution of the internal bioluminescence sources is directly established, which fundamentally avoids solving the ill-posed inverse problem iteratively. Compared with the previous reconstruction method based on multilayer perceptron, the training parameters in the 1DCNN are greatly reduced and the learning efficiency of the model is improved. Simulations verify the superiority and stability of the 1DCNN method, and the in vivo experimental results further show the potential of the proposed method in practical applications.

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K-Nearest Neighbor Based Locally Connected Network for Fast Morphological Reconstruction in Fluorescence Molecular Tomography

Cited by 46 publications

References 36 publications

From Small Scales to Large Scales: Distance-to-Measure Density based Geometric Analysis of Complex Data

From Small Scales to Large Scales: Distance-to-Measure Density based Geometric Analysis of Complex Data

Demonstration of Neural Networks to Reconstruct Temperatures from Simulated Fluorescent Data towards use in Bio-Microfluidics

Bioluminescence Tomography Based on One-Dimensional Convolutional Neural Networks

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