Improved Block Sparse Bayesian Learning Method Using K-Nearest Neighbor Strategy for Accurate Tumor Morphology Reconstruction in Bioluminescence Tomography

Yin, Lin; Wang, Kun; Tong, Tong; An, Yu; Meng, Hui; Yang, Xin; Tian, Jie

doi:10.1109/tbme.2019.2953732

Cited by 18 publications

(12 citation statements)

References 35 publications

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“…(a) Statistics-based methods; (b) spectrum-based methods; (c) model-based methods. Statistics-based methods commonly use the grayscale distributions of image regions to describe texture characteristics, such as the gray-level co-occurrence matrix method [4], the autocorrelation method [5], the morphology method [6], and the histogram feature statistics [7]. Spectrum-based methods focus on finding the textural structure of the texture image and are particularly suitable for textures with an obvious structure, such as the Fourier feature method [8], Gabor feature method [9], and wavelet feature method [10].…”

Section: Related Workmentioning

confidence: 99%

“…The network structure of MobileNet-v2-dense is formed by dense cascades between multi-scale channels, with six cascades. 1 2 , and 3 are cascades of feature maps with the same scale, 4 and 5 are cascades of feature maps with 1/4 down sampling, and 6 is a cascade of feature maps with 1/16 down sampling. Table 1.…”

Section: A Multi-scale Mobilenet-v2-dense Networkmentioning

confidence: 99%

“…In the past decades, many traditional methods of surface defect detection have been developed such as statistics-based methods [4][5][6][7], spectrum-based methods [8][9][10], and model-based methods [11][12][13]. These traditional detection algorithms rely on the features of artificial design, which are limited to specific defect modes.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Network for Surface Defect Detection

Lin

Zhan³

et al. 2020

Applied Sciences

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Convolutional neural networks (CNN) have achieved promising performance in surface defect detection recently. Although many CNN-based methods have been proposed, most of them are limited by the few samples available for training, and the imbalance of positive and negative samples. Hence, their detection performance needs to be further improved. To this end, we propose a multi-scale cascade CNN called MobileNet-v2-dense to detect defects more efficiently. Specifically, the multi-scale cascade structure used in our network can help capture the weak defect semantics that may be lost in the deep network. Then, we propose a novel asymmetric loss function to further improve detection performance. Lastly, a two-stage augmentation method effectively enlarges the training dataset. Experimental results show that, compared to the state-of-the-art, the area under the receiver-operating characteristic curve (AUC-ROC) score of our method increased by 0.16.

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Section: Related Workmentioning

confidence: 99%

Section: A Multi-scale Mobilenet-v2-dense Networkmentioning

confidence: 99%

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An Efficient Network for Surface Defect Detection

Lin

Zhan³

et al. 2020

Applied Sciences

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“…By assuming that the source distribution is piecewise‐constant, the level set algorithm and total variation regularization method have been proposed for solving the inverse problem of bioluminescence tomography . By considering the variance between any two voxels decreases with the increasing of their spatial distance, the Gaussian weighted Laplace prior regularization method and block sparse bayesian learning method were proposed for in vivo morphological imaging of glioma in BLT reconstruction . In this research, the shape‐constraint or energy‐constraint was introduced in the optimization function, which results in higher accuracy in tumor localization and morphology preservation.…”

Section: Introductionmentioning

confidence: 99%

Sparse‐graph manifold learning method for bioluminescence tomography

Guo

Gao

et al. 2020

Journal of Biophotonics

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In preclinical researches, bioluminescence tomography (BLT) has widely been used for tumor imaging and monitoring, imaged-guided tumor therapy, and so forth. For these biological applications, both tumor spatial location and morphology analysis are the leading problems needed to be taken into account. However, most existing BLT reconstruction methods were proposed for some specific applications with a focus on sparse representation or morphology recovery, respectively. How to design a versatile algorithm that can simultaneously deal with both aspects remains an impending problem. In this study, a Sparse-Graph Manifold Learning (SGML) method was proposed to balance the source sparseness and morphology, by integrating non-convex sparsity constraint and dynamic Laplacian graph model. Furthermore, based on the nonconvex optimization theory and some iterative approximation, we proposed a novel iteratively reweighted soft thresholding algorithm (IRSTA) to solve the SGML model. Numerical simulations and in vivo experiments result demonstrated that the proposed SGML method performed much superior to the comparative methods in spatial localization and tumor morphology recovery for various source settings. It is believed that the SGML method can be applied to the related optical tomography and facilitate the development of optical molecular tomography. K E Y W O R D Sbiology and medicine, bioluminescence tomography, image reconstruction techniques, inverse problems

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“…However, BLI can only detect the twodimensional body surface information, which is not sufficient to quantify the activity of tumor cells in the bodies of living animals. Bioluminescence tomography (BLT) employs threedimensional (3D) reconstruction of bioluminescent sources to more accurately locate and quantify tumors compared with BLI (5). The basic idea of BLT is to utilize a "forward" model of light propagation through the tissue to the skin surface, along with an "inversion" algorithm to reconstruct the underlying bioluminescence source distribution (6,7).…”

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confidence: 99%

Multispectral Differential Reconstruction Strategy for Bioluminescence Tomography

Chu

Guo

et al. 2022

Front. Oncol.

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Bioluminescence tomography (BLT) is a promising in vivo molecular imaging tool that allows non-invasive monitoring of physiological and pathological processes at the cellular and molecular levels. However, the accuracy of the BLT reconstruction is significantly affected by the forward modeling errors in the simplified photon propagation model, the measurement noise in data acquisition, and the inherent ill-posedness of the inverse problem. In this paper, we present a new multispectral differential strategy (MDS) on the basis of analyzing the errors generated from the simplification from radiative transfer equation (RTE) to diffusion approximation and data acquisition of the imaging system. Through rigorous theoretical analysis, we learn that spectral differential not only can eliminate the errors caused by the approximation of RTE and imaging system measurement noise but also can further increase the constraint condition and decrease the condition number of system matrix for reconstruction compared with traditional multispectral (TM) reconstruction strategy. In forward simulations, energy differences and cosine similarity of the measured surface light energy calculated by Monte Carlo (MC) and diffusion equation (DE) showed that MDS can reduce the systematic errors in the process of light transmission. In addition, in inverse simulations and in vivo experiments, the results demonstrated that MDS was able to alleviate the ill-posedness of the inverse problem of BLT. Thus, the MDS method had superior location accuracy, morphology recovery capability, and image contrast capability in the source reconstruction as compared with the TM method and spectral derivative (SD) method. In vivo experiments verified the practicability and effectiveness of the proposed method.

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Improved Block Sparse Bayesian Learning Method Using K-Nearest Neighbor Strategy for Accurate Tumor Morphology Reconstruction in Bioluminescence Tomography

Cited by 18 publications

References 35 publications

An Efficient Network for Surface Defect Detection

An Efficient Network for Surface Defect Detection

Sparse‐graph manifold learning method for bioluminescence tomography

Multispectral Differential Reconstruction Strategy for Bioluminescence Tomography

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