Browsing by Subject "fluorescence microscopy"

Now showing 1 - 10 of 10
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    Boundary Segmentation For Fluorescence Microscopy Using Steerable Filters
    (SPIE, 2017) Ho, David Joon; Salama, Paul; Dunn, Kenneth W.; Delp, Edward J.; Electrical and Computer Engineering, School of Engineering and Technology
    Fluorescence microscopy is used to image multiple subcellular structures in living cells which are not readily observed using conventional optical microscopy. Moreover, two-photon microscopy is widely used to image structures deeper in tissue. Recent advancement in fluorescence microscopy has enabled the generation of large data sets of images at different depths, times, and spectral channels. Thus, automatic object segmentation is necessary since manual segmentation would be inefficient and biased. However, automatic segmentation is still a challenging problem as regions of interest may not have well defined boundaries as well as non-uniform pixel intensities. This paper describes a method for segmenting tubular structures in fluorescence microscopy images of rat kidney and liver samples using adaptive histogram equalization, foreground/background segmentation, steerable filters to capture directional tendencies, and connected-component analysis. The results from several data sets demonstrate that our method can segment tubular boundaries successfully. Moreover, our method has better performance when compared to other popular image segmentation methods when using ground truth data obtained via manual segmentation.
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    Center-Extraction-Based Three Dimensional Nuclei Instance Segmentation of Fluorescence Microscopy Images
    (IEEE, 2019-05) Ho, David Joon; Han, Shuo; Fu, Chichen; Salama, Paul; Dunn, Kenneth W.; Delp, Edward J.; Electrical and Computer Engineering, School of Engineering and Technology
    Fluorescence microscopy is an essential tool for the analysis of 3D subcellular structures in tissue. An important step in the characterization of tissue involves nuclei segmentation. In this paper, a two-stage method for segmentation of nuclei using convolutional neural networks (CNNs) is described. In particular, since creating labeled volumes manually for training purposes is not practical due to the size and complexity of the 3D data sets, the paper describes a method for generating synthetic microscopy volumes based on a spatially constrained cycle-consistent adversarial network. The proposed method is tested on multiple real microscopy data sets and outperforms other commonly used segmentation techniques.
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    Nuclei counting in microscopy images with three dimensional generative adversarial networks
    (SPIE, 2019-03) Han, Shuo; Lee, Soonam; Fu, Chichen; Salama, Paul; Dunn, Kenneth W.; Delp, Edward J.; Electrical and Computer Engineering, School of Engineering and Technology
    Microscopy image analysis can provide substantial information for clinical study and understanding of biological structures. Two-photon microscopy is a type of fluorescence microscopy that can image deep into tissue with near-infrared excitation light. We are interested in methods that can detect and characterize nuclei in 3D fluorescence microscopy image volumes. In general, several challenges exist for counting nuclei in 3D image volumes. These include “crowding” and touching of nuclei, overlapping of nuclei, and shape and size variances of the nuclei. In this paper, a 3D nuclei counter using two different generative adversarial networks (GAN) is proposed and evaluated. Synthetic data that resembles real microscopy image is generated with a GAN and used to train another 3D GAN that counts the number of nuclei. Our approach is evaluated with respect to the number of groundtruth nuclei and compared with common ways of counting used in the biological research. Fluorescence microscopy 3D image volumes of rat kidneys are used to test our 3D nuclei counter. The accuracy results of proposed nuclei counter are compared with the ImageJ’s 3D object counter (JACoP) and the 3D watershed. Both the counting accuracy and the object-based evaluation show that the proposed technique is successful for counting nuclei in 3D.
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    Nuclei Segmentation of Fluorescence Microscopy Images Using Three Dimensional Convolutional Neural Networks
    (IEEE, 2017-07) Ho, David Joon; Fu, Chichen; Salama, Paul; Dunn, Kenneth W.; Delp, Edward J.; Electrical and Computer Engineering, School of Engineering and Technology
    Fluorescence microscopy enables one to visualize subcellular structures of living tissue or cells in three dimensions. This is especially true for two-photon microscopy using near-infrared light which can image deeper into tissue. To characterize and analyze biological structures, nuclei segmentation is a prerequisite step. Due to the complexity and size of the image data sets, manual segmentation is prohibitive. This paper describes a fully 3D nuclei segmentation method using three dimensional convolutional neural networks. To train the network, synthetic volumes with corresponding labeled volumes are automatically generated. Our results from multiple data sets demonstrate that our method can successfully segment nuclei in 3D.
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    O’ Brien Center
    (Office of the Vice Chancellor for Research, 2013-04-05) Atkinson, Simon J.; Dunn, Kenneth W.; Molitoris, Bruce A.
    The O’Brien Center for Advanced Renal Microscopy and Analysis is based around the Indiana Center for Biological Microscopy in Indianapolis (ICBM), and is supported by partnerships with Purdue University and the University of North Carolina. The Center acts as a national resource for investigators to apply state-of-the-art techniques in fluorescence microscopy to research in kidney biology and pathophysiology. Investigators have access to four microscope systems capable of multiphoton and confocal imaging and optimized for intravital imaging studies on rodents. Point-scanning and spinning-disk confocal systems are also available. Training and assistance with development of imaging protocols are available from expert staff at the ICBM. The Center emphasizes development of new and improved methods for imaging the kidney and seeks to disseminate these innovations as widely as possible amongst renal investigators. Currently, the Center is (1) developing new software for rendering, segmentation, analysis and stabilization of three-dimensional data from live imaging experiments; (2) developing new fluorescent probes and delivery methods optimized for intravital imaging studies in the kidney; and (3) exploring methods to increase the reach of multiphoton imaging in the kidney. Funding is available through the Center’s O’Brien Fellows Program to support short visits (one-two weeks) to Indianapolis for data collection, development of imaging protocols to address particular questions and for training in fluorescence microscopy and image analysis. The Center also offers instructional workshops in fluorescence microscopy and intravital imaging every two years. Current information about how to access the resources available through the Center is available at http://medicine.iupui.edu/nephrology/obrien.
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    Segmentation of fluorescence microscopy images using three dimensional active contours with inhomogeneity correction
    (IEEE, 2017-04) Lee, Soonam; Salama, Paul; Dunn, Kenneth W.; Delp, Edward J.; Electrical and Computer Engineering, School of Engineering and Technology
    Image segmentation is an important step in the quantitative analysis of fluorescence microscopy data. Since fluorescence microscopy volumes suffer from intensity inhomogeneity, low image contrast and limited depth resolution, poor edge details, and irregular structure shape, segmentation still remains a challenging problem. This paper describes a nuclei segmentation method for fluorescence microscopy based on the use of three dimensional (3D) active contours with inhomogeneity correction. The correction information utilizes 3D volume information while addressing intensity inhomogeneity across vertical and horizontal directions. Experimental results demonstrate that the proposed method achieves better performance than other reported methods.
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    Three Dimensional Blind Image Deconvolution for Fluorescence Microscopy using Generative Adversarial Networks
    (IEEE, 2019) Lee, Soonam; Han, Shuo; Salama, Paul; Dunn, Kenneth W.; Delp, Edward J.; Electrical and Computer Engineering, School of Engineering and Technology
    Due to image blurring image deconvolution is often used for studying biological structures in fluorescence microscopy. Fluorescence microscopy image volumes inherently suffer from intensity inhomogeneity, blur, and are corrupted by various types of noise which exacerbate image quality at deeper tissue depth. Therefore, quantitative analysis of fluorescence microscopy in deeper tissue still remains a challenge. This paper presents a three dimensional blind image deconvolution method for fluorescence microscopy using 3way spatially constrained cycle-consistent adversarial networks. The restored volumes of the proposed deconvolution method and other well-known deconvolution methods, denoising methods, and an inhomogeneity correction method are visually and numerically evaluated. Experimental results indicate that the proposed method can restore and improve the quality of blurred and noisy deep depth microscopy image visually and quantitatively.
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    Three Dimensional Nuclei Segmentation and Classification of Fluorescence Microscopy Images
    (IEEE, 2020-04) Han, Shuo; Lee, Soonam; Chen, Alain; Yang, Changye; Salama, Paul; Dunn, Kenneth W.; Delp, Edward J.; Electrical and Computer Engineering, School of Engineering and Technology
    Segmentation and classification of cell nuclei in fluorescence 3D microscopy image volumes are fundamental steps for image analysis. However, accurate cell nuclei segmentation and detection in microscopy image volumes are hampered by poor image quality, crowding of nuclei, and large variation in nuclei size and shape. In this paper, we present an unsupervised volume to volume translation approach adapted from the Recycle-GAN using modified Hausdorff distance loss for synthetically generating nuclei with better shapes. A 3D CNN with a regularization term is used for nuclei segmentation and classification followed by nuclei boundary refinement. Experimental results demonstrate that the proposed method can successfully segment nuclei and identify individual nuclei.
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    Tubule Segmentation of Fluorescence Microscopy Images Based on Convolutional Neural Networks With Inhomogeneity Correction
    (Society for Imaging Science and Technology, 2018) Lee, Soonam; Fu, Chichen; Salama, Paul; Dunn, Kenneth W.; Delp, Edward J.; Electrical and Computer Engineering, School of Engineering and Technology
    Fluorescence microscopy has become a widely used tool for studying various biological structures of in vivo tissue or cells. However, quantitative analysis of these biological structures remains a challenge due to their complexity which is exacerbated by distortions caused by lens aberrations and light scattering. Moreover, manual quantification of such image volumes is an intractable and error-prone process, making the need for automated image analysis methods crucial. This paper describes a segmentation method for tubular structures in fluorescence microscopy images using convolutional neural networks with data augmentation and inhomogeneity correction. The segmentation results of the proposed method are visually and numerically compared with other microscopy segmentation methods. Experimental results indicate that the proposed method has better performance with correctly segmenting and identifying multiple tubular structures compared to other methods.
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    Using quantitative intravital multiphoton microscopy to dissect hepatic transport in rats
    (Elsevier, 2017) Dunn, Kenneth W.; Ryan, Jennifer C.; Department of Medicine, IU School of Medicine
    Hepatic solute transport is a complex process whose disruption is associated with liver disease and drug-induced liver injury. Intravital multiphoton fluorescence excitation microscopy provides the spatial and temporal resolution necessary to characterize hepatic transport at the level of individual hepatocytes in vivo and thus to identify the mechanisms and cellular consequences of cholestasis. Here we present an overview of the use of fluorescence microscopy for studies of hepatic transport in living animals, and describe how we have combined methods of intravital microscopy and digital image analysis to dissect the effects of drugs and pathological conditions on the function of hepatic transporters in vivo.