High-throughput <i>in vitro</i> screening of highly physiological three-dimensional cell cultures (3D-HTS) is rapidly gaining importance in preclinical studies, to study the effect of the microenvironment in tumor development, and to evaluate the efficacy of new anticancer drugs. Furthermore, it could also be envisioned the use of 3D-HTS systems in personalized anti-cancer treatment planning, based on tumor organoids or spheroids grown from tumor biopsies or isolated tumor circulating cells. Most commercial, multi-well plate based 3D-HTS systems are large, expensive, and are based on the use of multi-well plates that hardly provide a physiological environment and require the use of large amounts of biological material and reagents. In this paper we present a novel, miniaturized inverted microscope (hereinafter miniscospe), made up of low-cost, mass producible parts, that can be used to monitor the growth of living tumor cell spheroids within customized three-dimensional microfluidic platforms. Our 3D-HTS miniscope combines phase contrast imaging based on oblique back illumination technique with traditional widefield epi-fluorescence imaging, implemented using miniaturized electro-optical parts and gradient-index refraction lenses. This small (3x6x2cm), lightweight device can effectively image overtime the growth of (>200) tumor spheroids in a controlled and reproducible environment. Our miniscope can be used to acquire time-lapse images of cellular living spheroids over the course of several hours and captures their growth before and after drug treatment, to evaluate the effectiveness of the drug.
Accurate quantification of vessel diameter in low-dose Computer Tomography (CT) images is important to study pulmonary diseases, in particular for the diagnosis of vascular diseases and the characterization of morphological vascular remodeling in Chronic Obstructive Pulmonary Disease (COPD). In this study, we objectively compare several vessel diameter estimation methods using a physical phantom. Five solid tubes of differing diameters (from 0.898 to 3.980 mm) were embedded in foam, simulating vessels in the lungs. To measure the diameters, we first extracted the vessels using either of two approaches: vessel enhancement using multi-scale Hessian matrix computation, or explicitly segmenting them using intensity threshold. We implemented six methods to quantify the diameter: three estimating diameter as a function of scale used to calculate the Hessian matrix; two calculating equivalent diameter from the crosssection area obtained by thresholding the intensity and vesselness response, respectively; and finally, estimating the diameter of the object using the Full Width Half Maximum (FWHM). We find that the accuracy of frequently used methods estimating vessel diameter from the multi-scale vesselness filter depends on the range and the number of scales used. Moreover, these methods still yield a significant error margin on the challenging estimation of the smallest diameter (on the order or below the size of the CT point spread function). Obviously, the performance of the thresholding-based methods depends on the value of the threshold. Finally, we observe that a simple adaptive thresholding approach can achieve a robust and accurate estimation of the smallest vessels diameter.
We present a method for the automatic delineation of two neuromelanin rich brainstem structures –<i>substantia nigra pars </i><i>compacta</i> (SN) and <i>locus coeruleus </i>(LC)- in neuromelanin sensitive magnetic resonance images of the brain. The segmentation method uses a dynamic multi-image reference atlas and a pre-registration atlas selection strategy. To create the atlas, a pool of 35 images of healthy subjects was pair-wise pre-registered and clustered in groups using an affinity propagation approach. Each group of the atlas is represented by a single exemplar image. Each new target image to be segmented is registered to the exemplars of each cluster. Then all the images of the highest performing clusters are enrolled into the final atlas, and the results of the registration with the target image are propagated using a majority voting approach. All registration processes used combined one two-stage affine and one elastic B-spline algorithm, to account for global positioning, region selection and local anatomic differences. In this paper, we present the algorithm, with emphasis in the atlas selection method and the registration scheme. We evaluate the performance of the atlas selection strategy using 35 healthy subjects and 5 Parkinson’s disease patients. Then, we quantified the volume and contrast ratio of neuromelanin signal of these structures in 47 normal subjects and 40 Parkinson’s disease patients to confirm that this method can detect neuromelanin-containing neurons loss in Parkinson’s disease patients and could eventually be used for the early detection of SN and LC damage.
Emphysema is associated with the destruction of lung parenchyma, resulting in abnormal enlargement of airspaces. Accurate quantification of emphysema is required for a better understanding of the disease as well as for the assessment of drugs and treatments. In the present study, a novel method for emphysema characterization from histological lung images is proposed. Elastase-induced mice were used to simulate the effect of emphysema on the lungs. A database composed of 50 normal and 50 emphysematous lung patches of size 512 x 512 pixels was used in our experiments. The purpose is to automatically identify those patches containing emphysematous tissue. The proposed approach is based on the use of granulometry analysis, which provides the pattern spectrum describing the distribution of airspaces in the lung region under evaluation. The profile of the spectrum was summarized by a set of statistical features. A logistic regression model was then used to estimate the probability for a patch to be emphysematous from this feature set. An accuracy of 87% was achieved by our method in the classification between normal and emphysematous samples. This result shows the utility of our granulometry-based method to quantify the lesions due to emphysema.
In this study, we quantitatively characterize lung airway remodeling caused by smoking-related emphysema and Chronic
Obstructive Pulmonary Disease (COPD), in low-dose CT scans. To that end, we established three groups of individuals:
subjects with COPD (n=35), subjects with emphysema (n=38) and healthy smokers (n=28). All individuals underwent a
low-dose CT scan, and the images were analyzed as described next. First the lung airways were segmented using a fast
marching method and labeled according to its generation. Along each airway segment, cross-section images were
resampled orthogonal to the airway axis. Next 128 rays were cast from the center of the airway lumen in each crosssection
slice. Finally, we used an integral-based method, to measure lumen radius, wall thickness, mean wall percentage
and mean peak wall attenuation on every cast ray. Our analysis shows that both the mean global wall thickness and the
lumen radius of the airways of both COPD and emphysema groups were significantly different from those of the healthy
group. In addition, the wall thickness change starts at the 3<sup>rd</sup> airway generation in the COPD patients compared with
emphysema patients, who display the first significant changes starting in the 2<sup>nd</sup> generation. In conclusion, it is shown
that airway remodeling happens in individuals suffering from either COPD or emphysema, with some local difference
between both groups, and that we are able to detect and accurately quantify this process using images of low-dose CT
In this paper, we explore the use of anatomical information as a guide in the image formation
process of fluorescence molecular tomography (FMT). Namely, anatomical knowledge obtained
from high resolution computed tomography (micro-CT) is used to construct a model for the
diffusion of light and to constrain the reconstruction to areas candidate to contain fluorescent
volumes. Moreover, a sparse regularization term is added to the state-of-the-art least square
solution to contribute to the sparsity of the localization. We present results showing the increase
in accuracy of the combined system over conventional FMT, for a simulated experiment of lung
cancer detection in mice.
Proc. SPIE. 7262, Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging
KEYWORDS: Image processing algorithms and systems, Signal to noise ratio, Image segmentation, Wavefronts, Image analysis, Lung, Wave propagation, Computed tomography, Reconstruction algorithms, In vivo imaging
Mouse models are becoming instrumental for the study of lung disease. Due to its resolution and low cost, high resolution Computed Tomography (micro-CT) is a very adequate technology to visualize the mouse lungs in-vivo. Automatic segmentation and measurement of airways in micro-CT images of the lungs can be useful as a preliminary step prior other image analysis quantification tasks, as well as for the study of pathologies that alter the airways structure. In this paper, we present an efficient segmentation and reconstruction algorithm which simultaneously segments and reconstructs the bronchial tree, while providing the length and mean radius of each airway segment. A locally adaptive intensity threshold is used to account for the low signal to noise ratio and strong artifacts present in micro-CT images. We validate our method by comparing it with manual segmentations of 10 different scans, obtaining an average true positive volume fraction of 85.52% with a false positive volume fraction of 5.04%.
Atlas-based segmentation has proven effective in multiple applications. Usually, several reference images are combined
to create a representative average atlas image. Alternatively, a number of independent atlas images can be used, from which multiple segmentations of the image of interest are derived and later combined. One of the major drawbacks of this approach is its large computational burden caused by the high number of required registrations. To address this problem, we introduce One Registration, Multiple Segmentations (ORMS), a procedure to obtain multiple segmentations with a single online registration. This can be achieved by pre-computing intermediate transformations from the initial atlas images to an average image. We show that, compared to the usual approach, our method reduces time considerably
with little or no loss in accuracy. On the other hand, optimum combination of these segmentations remains an unresolved problem. Different approaches have been adopted, but they are all far from the upper bound of any combination strategy. This is given by the
Combination Oracle, which classifies a voxel correctly if any individual segmentation coincides with the ground truth.
We present here a novel combination approach, based on weighting the different segmentations according to the mutual information between the test image and the atlas image after registration. We compare this method with other existing combination strategies using microscopic MR images of mouse brains, achieving statistically significant improvement in segmentation accuracy.
Real-time three-dimensional (3-D) reconstruction of epithelial structures in human mammary gland tissue blocks mapped with selected markers would be an extremely helpful tool for diagnosing breast cancer and planning treatment. Besides its clear clinical application, this tool could also shed a great deal of light on the molecular basis of the initiation and progression of breast cancer. We present a framework for real-time segmentation of epithelial structures in two-dimensional (2-D) images of sections of normal and neoplastic mammary gland tissue blocks. Complete 3-D rendering of the tissue can then be done by surface rendering of the structures detected in consecutive sections of the blocks. Paraffin-embedded or frozen tissue blocks are first sliced and sections are stained with hematoxylin and eosin. The sections are then imaged using conventional bright-field microscopy and their background corrected using a phantom image. We then use the fast-marching algorithm to roughly extract the contours of the different morphological structures in the images. The result is then refined with the level-set method, which converges to an accurate (subpixel) solution for the segmentation problem. Finally, our system stacks together the 2-D results obtained in order to reconstruct a 3-D representation of the entire tissue block under study. Our method is illustrated with results from the segmentation of human and mouse mammary gland tissue samples.
In this paper we present a scheme for real time segmentation of histological structures in microscopic images of normal and neoplastic mammary gland sections. Paraffin embedded or frozen tissue blocks are sliced, and sections are stained with hematoxylin and eosin (H&E). The sections are then imaged using conventional bright field microscopy. The background of the images is corrected by arithmetic manipulation using a "phantom." Then we use the fast marching method with a speed function that depends on the brightness gradient of the image to obtain a preliminary approximation to the boundaries of the structures of interest within a region of interest (ROI) of the entire section manually selected by the user. We use the result of the fast marching method as the initial condition for the level set motion equation. We run this last method for a few steps and obtain the final result of the segmentation. These results can be connected from section to section to build a three-dimensional reconstruction of the entire tissue block that we are studying.
Quantitative analysis of spatial and temporal concurrent responses of multiple markers in 3-dimensional cell cultures is hampered by the routine mode of sequential image acquisition, measurement and analysis of specific targets. A system was developed for detailed analysis of multi-dimensional, time-sequence responses and in order to relate features in novel and meaningful ways that will further our understanding of basic biology. Optical sectioning of the 3-dimensional structures is achieved with structured light illumination using the Wilson grating as described by Lanni. The automated microscopy system can image multicellular structures and track dynamic events, and is equipped for simultaneous/ sequential imaging of multiple fluorescent markers. Computer-controlled perfusion of external stimuli into the culture system allows (i) real-time observations of multiple cellular responses and (ii) automatic and intelligent adjustment of experimental parameters. This creates a feedback loop in real-time that directs desired responses in a given experiment. On-line image analysis routines provide cell-by-cell measurement results through segmentation and feature extraction (i.e. intensity, localization, etc.), and quantitation of meta-features such as dynamic responses of cells or correlations between different cells. Off-line image and data analysis is used to derive models of the processes involved, which will deepen the understanding of the basic biology.
Advancements in image analysis shave recently made it possible to segment the cells and nuclei, of a wide variety of tissues, from 3D images collected using fluorescence confocal microscopy. This has made it possible to analyze the spatial organization of individual cells and nuclei within the natural tissue context. We present here a spatial statistical method which examines an arbitrary 3D distribution of cells of two different types and determines the probability that the cells are randomly mixed, cells of one type are clustered, or cells of different types are preferentially associated. Beginning with a segmented 3D image of cells, the Voronoi diagram is calculated to indicate the nearest neighbor relationships of the cells. Then, in a test image of the same topology, cells are randomly assigned a type in the same proportions as in the actual specimen and the ratio of cells with nearest neighbors of the same type versus the other types is calculated. Repetition of this random assignment is used to generate a distribution function which is specific for the tissue image. Comparison of the ratios for the actual sample to this distribution assigns probabilities for the conditions defined above. The technique is being used to analyze the organization of genetically normal versus abnormal cells in cancer tissue.
The aim of this work is to show that properly trained ANNs can be used as an image restoration tool to correct the effect of defocusing on 2D optical microscopy images. The proposed method can be applied to correct the results of inaccurate range focusing algorithms on fully automated imaging and analysis systems and to compensate the effect of the limited of the depth of focusing on high numerical aperture system. One type of ANN was used: feedforward multilayer perceptron, with supervised back propagation training. Its performance has been tested with both synthetic images and real images from latex microspheres. The network was trained with sets of pairs of images: each pair consisted of a defocused image and its corresponding in-focus version. Different levels of defocusing were used. The criteria used to select the algorithm parameters to tune the networks and to train them will be presented. The results of the experiments performed to test their ability to 'learn' to correct the defocusing and to generalize the results will also be shown. The result show that, when trained with images with some levels of defocusing, the network was able to learn and accurately correct these defocusing levels, but it can also generalize the results and correct other levels of defocusing.