Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related death worldwide. The high probability of metastasis makes its prognosis very poor even after potentially curative treatment. Detecting high metastatic HCC will allow for the development of effective approaches to reduce HCC mortality. The mechanism of HCC metastasis has been studied using gene profiling analysis, which indicated that HCC with different metastatic capability was differentiable. However, it is time consuming and complex to analyze gene expression level with conventional method. To distinguish HCC with different metastatic capabilities, we proposed a deep learning based method with microscopic images in animal models. In this study, we adopted convolutional neural networks (CNN) to learn the deep features of microscopic images for classifying each image into low metastatic HCC or high metastatic HCC. We evaluated our proposed classification method on the dataset containing 1920 white-light microscopic images of frozen sections from three tumor-bearing mice injected with HCC-LM3 (high metastasis) tumor cells and another three tumor-bearing mice injected with SMMC-7721(low metastasis) tumor cells. Experimental results show that our method achieved an average accuracy of 0.85. The preliminary study demonstrated that our deep learning method has the potential to be applied to microscopic images for metastasis of HCC classification in animal models.
Fluorescence molecular tomography (FMT) is a promising imaging technique in applications of preclinical research. However, the complexity of radiative transfer equation (RTE) and the ill-poseness of the inverse problem limit the effectiveness of FMT reconstruction. In this research, we proposed a novel Deep Convolutional Neural Network (DCNN), Gated Recurrent Unit (GRU) and Multiple Layer Perception (MLP) based method (DGMM) for FMT reconstruction. Instead of establishing the photon transmission models and solving the inverse problem, the proposed method directly fits the nonlinear relationship between fluorescence intensity at the boundary and fluorescent source in biological tissue. For details, DGMM consists of three stages: In the first stage, the measured optical intensity was encoded into a feature vector by transferring the VGG16 model; In the second stage, we fused all encoded feature vectors into one feature vector by using GRU based network; In the last stage, the fused feature vector was used to reconstruct the fluorescent sources by MLP model. To evaluate the performance of our proposed method, a 3D digital mouse was utilized to generate FMT Monte Carlo simulation samples. In quantitative analysis, the results demonstrated that DGMM method has comparable performance with conventional method in tumor position locating. To the best of our knowledge, this is the first study that employed DCNN based methods for FMT reconstruction, which holds a great potential of improving the reconstruction quality of FMT.
With the development of fluorescence molecular tomography (FMT), it has become an important tool for life science research. Many methods were developed to improve the reconstruction performance of FMT. However, these methods only focus on the accuracy and speed of the reconstruction, while ignoring the morphological information of the tumor. In this paper, the modified L2,1-norm method were adopted to reconstruct the morphology of the mouse orthotopic glioma. To verify the L2,1-norm method, we carried out the simulation experiment and the in vivo orthotopic glioma mouse experiment. The iterated shrinkage (IS) method and the Tikhonov-based method were implemented for comparison. The results show that the modified L2,1-norm method can obtain more accurate reconstruction in both tumor location and morphology reconstruction. We believe that the morphological reconstruction of FMT will provide more potential for preclinical research.
The development of Bioluminescence Tomography (BLT) has allowed the quantitative three dimension (3D) whole body imaging and non-invasive study of biological behavior of cancer. However, the ill-posed problem of BLT reconstruction limits the quality of reconstruction result. In this work, we proposed a Bilateral Weight Laplace (BWL) method that utilizes a non-local Laplace regularization to improve the imaging quality of bioluminescence tomography reconstruction. The non-local Laplace regularization was constructed by spatial weight and range weight to penalize the neighborhood-variance of reconstructed source density in both spatial and range domain. To evaluate the performance of BWL method, both dual-source BLT reconstruction experiment in simulation data and in vivo BLT reconstruction in orthotopic glioma mouse model were designed. Furthermore, fast iterative shrinkage/threshold (FIST) method and Laplace method were utilized to compare with BWL. Both dual-source experiment and in vivo experiment demonstrate that the construction results of BWL method provide more accurate tumor position (BCE = 0.37mm in dual-source experiment) and better tumor morphological information.
The high sensitivity and low cost of fluorescence imaging enables fluorescence molecular tomography (FMT) as a powerful noninvasive technique in applications of tracer distribution visualization. With the development of targeted fluorescence tracer, FMT has been widely used to localize the tumor. However, the visualization of probe distribution in tumor and surrounding region is still a challenge for FMT reconstruction. In this study, we proposed a novel nonlocal total variation (NLTV) regularization method, which is based on structure prior information. To build the NLTV regularization term, we consider the first order difference between the voxel and its four nearest neighbors. Furthermore, we assume that the variance of fluorescence intensity between any two voxels has a non-linear inverse correlation with their Gaussian distance. We adopted the Gaussian distance between two voxels as the weight of the first order difference. Meanwhile, the split Bregman method was applied to minimize the optimization problem. To evaluate the robustness and feasibility of our proposed method, we designed numerical simulation experiments and in vivo experiments of xenograft orthotopic glioma models. The ex vivo fluorescent images of cryoslicing specimens were regarded as gold standard of probe distribution in biological tissue. The results demonstrated that the proposed method could recover the morphology of the tracer distribution more accurately compared with fast iterated shrinkage (FIS) method, Split Bregman-resolved TV (SBRTV) regularization method and Gaussian weighted Laplace prior (GWLP) regularization method. These results demonstrate the potential of our method for in vivo visualization of tracer distribution in xenograft orthotopic glioma models.
Fluorescence molecular tomography (FMT) has been widely used in preclinical tumor imaging, which enables three-dimensional imaging of the distribution of fluorescent probes in small animal bodies via image reconstruction method. However, the reconstruction results are usually unsatisfactory in the term of robustness and efficiency because of the ill-posed and ill-conditioned of FMT problem. In this study, an FMT reconstruction method based on primal accelerated proximal gradient (PAPG) descent and L1-norm regularized projection (L1RP) is proposed. The proposed method utilizes the current and previous iterations to obtain a search point at each iteration. To achieve fast convergence, the PAPG method is applied to efficiently solve the search point, and then L1RP is performed to obtain the robust and accurate reconstruction. To verify the performance of the proposed method, simulation experiments are conducted. The comparative results revealed that it held advantages of robustness, accuracy, and efficiency in FMT reconstructions. Furthermore, a phantom experiment and an in vivo mouse experiment were also performed, which proved the potential and feasibility of the proposed method for practical applications.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.