Nuclear proliferomics: A new field of study to identify signatures of nuclear materials as demonstrated on alpha-UO3|
I. .J Schwerdt, A. Brenkmann, S. Martinson, B. D. Albrecht, S. Heffernan, M. R. Klosterman, T. Kirkham, T. Tasdizen, L. W. McDonald IV. In Talanta, Vol. 186, Elsevier BV, pp. 433--444. Aug, 2018.
The use of a limited set of signatures in nuclear forensics and nuclear safeguards may reduce the discriminating power for identifying unknown nuclear materials, or for verifying processing at existing facilities. Nuclear proliferomics is a proposed new field of study that advocates for the acquisition of large databases of nuclear material properties from a variety of analytical techniques. As demonstrated on a common uranium trioxide polymorph, α-UO3, in this paper, nuclear proliferomics increases the ability to improve confidence in identifying the processing history of nuclear materials. Specifically, α-UO3 was investigated from the calcination of unwashed uranyl peroxide at 350, 400, 450, 500, and 550 °C in air. Scanning electron microscopy (SEM) images were acquired of the surface morphology, and distinct qualitative differences are presented between unwashed and washed uranyl peroxide, as well as the calcination products from the unwashed uranyl peroxide at the investigated temperatures. Differential scanning calorimetry (DSC), UV–Vis spectrophotometry, powder X-ray diffraction (p-XRD), and thermogravimetric analysis-mass spectrometry (TGA-MS) were used to understand the source of these morphological differences as a function of calcination temperature. Additionally, the SEM images were manually segmented using Morphological Analysis for MAterials (MAMA) software to identify quantifiable differences in morphology for three different surface features present on the unwashed uranyl peroxide calcination products. No single quantifiable signature was sufficient to discern all calcination temperatures with a high degree of confidence; therefore, advanced statistical analysis was performed to allow the combination of a number of quantitative signatures, with their associated uncertainties, to allow for complete discernment by calcination history. Furthermore, machine learning was applied to the acquired SEM images to demonstrate automated discernment with at least 89% accuracy.
ISAVS: Interactive Scalable Analysis and Visualization System|
S. Petruzza, A. Venkat, A. Gyulassy, G. Scorzelli, F. Federer, A. Angelucci, V. Pascucci, P. T. Bremer. In ACM SIGGRAPH Asia 2017 Symposium on Visualization, ACM Press, 2017.
Modern science is inundated with ever increasing data sizes as computational capabilities and image acquisition techniques continue to improve. For example, simulations are tackling ever larger domains with higher fidelity, and high-throughput microscopy techniques generate larger data that are fundamental to gather biologically and medically relevant insights. As the image sizes exceed memory, and even sometimes local disk space, each step in a scientific workflow is impacted. Current software solutions enable data exploration with limited interactivity for visualization and analytic tasks. Furthermore analysis on HPC systems often require complex hand-written parallel implementations of algorithms that suffer from poor portability and maintainability. We present a software infrastructure that simplifies end-to-end visualization and analysis of massive data. First, a hierarchical streaming data access layer enables interactive exploration of remote data, with fast data fetching to test analytics on subsets of the data. Second, a library simplifies the process of developing new analytics algorithms, allowing users to rapidly prototype new approaches and deploy them in an HPC setting. Third, a scalable runtime system automates mapping analysis algorithms to whatever computational hardware is available, reducing the complexity of developing scaling algorithms. We demonstrate the usability and performance of our system using a use case from neuroscience: filtering, registration, and visualization of tera-scale microscopy data. We evaluate the performance of our system using a leadership-class supercomputer, Shaheen II.
|Longitudinal Modeling of Multi-modal Image Contrast Reveals Patterns of Early Brain Growth,
A. Vardhan, J. Fishbaugh, C. Vachet, G. Gerig. In Medical Image Computing and Computer Assisted Intervention - MICCAI 2017, Springer International Publishing, pp. 75--83. 2017.
The brain undergoes rapid development during early childhood as a series of biophysical and chemical processes occur, which can be observed in magnetic resonance (MR) images as a change over time of white matter intensity relative to gray matter. Such a contrast change manifests in specific patterns in different imaging modalities, suggesting that brain maturation is encoded by appearance changes in multi-modal MRI. In this paper, we explore the patterns of early brain growth encoded by multi-modal contrast changes in a longitudinal study of children. For a given modality, contrast is measured by comparing histograms of intensity distributions between white and gray matter. Multivariate non-linear mixed effects (NLME) modeling provides subject-specific as well as population growth trajectories which accounts for contrast from multiple modalities. The multivariate NLME procedure and resulting non-linear contrast functions enable the study of maturation in various regions of interest. Our analysis of several brain regions in a study of 70 healthy children reveals a posterior to anterior pattern of timing of maturation in the major lobes of the cerebral cortex, with posterior regions maturing earlier than anterior regions. Furthermore, we find significant differences between maturation rates between males and females.
Neural circuitry at age 6~months associated with later repetitive behavior and sensory responsiveness in autism|
J. J. Wolff, M. R. Swanson, J. T. Elison, G. Gerig, J. R. Pruett, M. A. Styner, C. Vachet, K. N. Botteron, S. R. Dager, A. M. Estes, H. C. Hazlett, R. T. Schultz, M. D. Shen, L. Zwaigenbaum, J. Piven. In Molecular Autism, Vol. 8, No. 1, Springer Nature, March, 2017.
|Rank Constrained Diffeomorphic Density Motion Estimation for Respiratory Correlated Computed Tomography,
M. Foote, P. Sabouri, A. Sawant, S. Joshi. In Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics, Springer International Publishing, pp. 177--185. 2017.
Motion estimation of organs in a sequence of images is important in numerous medical imaging applications. The focus of this paper is the analysis of 4D Respiratory Correlated Computed Tomography (RCCT) Imaging. It is hypothesized that the quasi-periodic breathing induced motion of organs in the thorax can be represented by deformations spanning a very low dimension subspace of the full infinite dimensional space of diffeomorphic transformations. This paper presents a novel motion estimation algorithm that includes the constraint for low-rank motion between the different phases of the RCCT images. Low-rank deformation solutions are necessary for the efficient statistical analysis and improved treatment planning and delivery. Although the application focus of this paper is RCCT the algorithm is quite general and applicable to various motion estimation problems in medical imaging.
|Revisiting Abnormalities in Brain Network Architecture Underlying Autism Using Topology-Inspired Statistical Inference,
S. Palande, V. Jose, B. Zielinski, J. Anderson, P.T. Fletcher, B. Wang. In Connectomics in NeuroImaging, Springer International Publishing, pp. 98--107. 2017.
A large body of evidence relates autism with abnormal structural and functional brain connectivity. Structural covariance MRI (scMRI) is a technique that maps brain regions with covarying gray matter density across subjects. It provides a way to probe the anatomical structures underlying intrinsic connectivity networks (ICNs) through the analysis of the gray matter signal covariance. In this paper, we apply topological data analysis in conjunction with scMRI to explore network-specific differences in the gray matter structure in subjects with autism versus age-, gender- and IQ-matched controls. Specifically, we investigate topological differences in gray matter structures captured by structural covariance networks (SCNs) derived from three ICNs strongly implicated in autism, namely, the salience network (SN), the default mode network (DMN) and the executive control network (ECN). By combining topological data analysis with statistical inference, our results provide evidence of statistically significant network-specific structural abnormalities in autism, from SCNs derived from SN and ECN. These differences in brain architecture are consistent with direct structural analysis using scMRI (Zielinski et al. 2012).
Nonparametric joint shape and feature priors for image segmentation|
E. Erdil, M.U. Ghani, L. Rada, A.O. Argunsah, D. Unay, T. Tasdizen, M. Cetin. In IEEE Transactions on Image Processing, Vol. 26, No. 11, IEEE, pp. 5312--5323. Nov, 2017.
In many image segmentation problems involving limited and low-quality data, employing statistical prior information about the shapes of the objects to be segmented can significantly improve the segmentation result. However, defining probability densities in the space of shapes is an open and challenging problem, especially if the object to be segmented comes from a shape density involving multiple modes (classes). Existing techniques in the literature estimate the underlying shape distribution by extending Parzen density estimator to the space of shapes. In these methods, the evolving curve may converge to a shape from a wrong mode of the posterior density when the observed intensities provide very little information about the object boundaries. In such scenarios, employing both shape- and class-dependent discriminative feature priors can aid the segmentation process. Such features may involve, e.g., intensity-based, textural, or geometric information about the objects to be segmented. In this paper, we propose a segmentation algorithm that uses nonparametric joint shape and feature priors constructed by Parzen density estimation. We incorporate the learned joint shape and feature prior distribution into a maximum a posteriori estimation framework for segmentation. The resulting optimization problem is solved using active contours. We present experimental results on a variety of synthetic and real data sets from several fields involving multimodal shape densities. Experimental results demonstrate the potential of the proposed method.
Shape analysis of the basioccipital bone in Pax7-deficient mice|
J. Cates, L. Nevell, S. I. Prajapati, L. D. Nelon, J. Y. Chang, M. E. Randolph, B. Wood, C. Keller, R. T. Whitaker. In Scientific Reports, Vol. 7, No. 1, Springer Nature, Dec, 2017.
We compared the cranial base of newborn Pax7-deficient and wildtype mice using a computational shape modeling technology called particle-based modeling (PBM). We found systematic differences in the morphology of the basiooccipital bone, including a broadening of the basioccipital bone and an antero-inferior inflection of its posterior edge in the Pax7-deficient mice. We show that the Pax7 cell lineage contributes to the basioccipital bone and that the location of the Pax7 lineage correlates with the morphology most effected by Pax7 deficiency. Our results suggest that the Pax7-deficient mouse may be a suitable model for investigating the genetic control of the location and orientation of the foramen magnum, and changes in the breadth of the basioccipital.
Dendritic spine shape analysis using disjunctive normal shape models|
M.U. Ghani, F. Mesadi, S..D Kanik, A.O. Argunsah, I. Israely, D. Unay, T. Tasdizen, M. Cetin. In 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), IEEE, April, 2016.
Analysis of dendritic spines is an essential task to understand the functional behavior of neurons. Their shape variations are known to be closely linked with neuronal activities. Spine shape analysis in particular, can assist neuroscientists to identify this relationship. A novel shape representation has been proposed recently, called Disjunctive Normal Shape Models (DNSM). DNSM is a parametric shape representation and has proven to be successful in several segmentation problems. In this paper, we apply this parametric shape representation as a feature extraction algorithm. Further, we propose a kernel density estimation (KDE) based classification approach for dendritic spine classification. We evaluate our proposed approach on a data set of 242 spines, and observe that it outperforms the classical morphological feature based approach for spine classification. Our probabilistic framework also provides a way to examine the separability of spine shape classes in the likelihood ratio space, which leads to further insights about the nature of the shape analysis problem in this context.
On comparison of manifold learning techniques for dendritic spine classification|
M.U. Ghani, A.O. Argunsah, I. Israely, D. Unay, T. Tasdizen, M. Cetin. In 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), IEEE, April, 2016.
Dendritic spines are one of the key functional components of neurons. Their morphological changes are correlated with neuronal activity. Neuroscientists study spine shape variations to understand their relation with neuronal activity. Currently this analysis performed manually, the availability of reliable automated tools would assist neuroscientists and accelerate this research. Previously, morphological features based spine analysis has been performed and reported in the literature. In this paper, we explore the idea of using and comparing manifold learning techniques for classifying spine shapes. We start with automatically segmented data and construct our feature vector by stacking and concatenating the columns of images. Further, we apply unsupervised manifold learning algorithms and compare their performance in the context of dendritic spine classification. We achieved 85.95% accuracy on a dataset of 242 automatically segmented mushroom and stubby spines. We also observed that ISOMAP implicitly computes prominent features suitable for classification purposes.
Nonparametric joint shape and feature priors for segmentation of dendritic spines|
E. Erdil, L. Rada, A.O. Argunsah, D. Unay, T. Tasdizen, M. Cetin. In 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), IEEE, April, 2016.
Multimodal shape density estimation is a challenging task in many biomedical image segmentation problems. Existing techniques in the literature estimate the underlying shape distribution by extending Parzen density estimator to the space of shapes. Such density estimates are only expressed in terms of distances between shapes which may not be sufficient for ensuring accurate segmentation when the observed intensities provide very little information about the object boundaries. In such scenarios, employing additional shape-dependent discriminative features as priors and exploiting both shape and feature priors can aid to the segmentation process. In this paper, we propose a segmentation algorithm that uses nonparametric joint shape and feature priors using Parzen density estimator. The joint prior density estimate is expressed in terms of distances between shapes and distances between features. We incorporate the learned joint shape and feature prior distribution into a maximum a posteriori estimation framework for segmentation. The resulting optimization problem is solved using active contours. We present experimental results on dendritic spine segmentation in 2-photon microscopy images which involve a multimodal shape density.
MCMC Shape Sampling for Image Segmentation with Nonparametric Shape Priors|
E. Erdil, M. Cetin, T. Tasdizen. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, June, 2016.
Segmenting images of low quality or with missing data is a challenging problem. Integrating statistical prior information about the shapes to be segmented can improve the segmentation results significantly. Most shape-based segmentation algorithms optimize an energy functional and find a point estimate for the object to be segmented. This does not provide a measure of the degree of confidence in that result, neither does it provide a picture of other probable solutions based on the data and the priors. With a statistical view, addressing these issues would involve the problem of characterizing the posterior densities of the shapes of the objects to be segmented. For such characterization, we propose a Markov chain Monte Carlo (MCMC) sampling-based image segmentation algorithm that uses statistical shape priors. In addition to better characterization of the statistical structure of the problem, such an approach would also have the potential to address issues with getting stuck at local optima, suffered by existing shape-based segmentation methods. Our approach is able to characterize the posterior probability density in the space of shapes through its samples, and to return multiple solutions, potentially from different modes of a multimodal probability density, which would be encountered, e.g., in segmenting objects from multiple shape classes. We present promising results on a variety of data sets. We also provide an extension for segmenting shapes of objects with parts that can go through independent shape variations. This extension involves the use of local shape priors on object parts and provides robustness to limitations in shape training data size.
Disjunctive Normal Unsupervised LDA for P300-based Brain-Computer Interfaces|
M. Elwardy, T. Tasdizen, M. Cetin. In 2016 24th Signal Processing and Communication Application Conference (SIU), IEEE, May, 2016.
Can people use text-entry based brain-computer interface (BCI) systems and start a free spelling mode without any calibration session? Brain activities differ largely across people and across sessions for the same user. Thus, how can the text-entry system classify the desired character among the other characters in the P300-based BCI speller matrix? In this paper, we introduce a new unsupervised classifier for a P300-based BCI speller, which uses a disjunctive normal form representation to define an energy function involving a logistic sigmoid function for classification. Our proposed classifier updates the initialized random weights performing classification for the P300 signals from the recorded data exploiting the knowledge of the sequence of row/column highlights. To verify the effectiveness of the proposed method, we performed an experimental analysis on data from 7 healthy subjects, collected in our laboratory. We compare the proposed unsupervised method to a baseline supervised linear discriminant analysis (LDA) classifier and demonstrate its effectiveness.
Disjunctive normal level set: An efficient parametric implicit method|
F. Mesadi, M. Cetin, T. Tasdizen. In 2016 IEEE International Conference on Image Processing (ICIP), IEEE, September, 2016.
Level set methods are widely used for image segmentation because of their capability to handle topological changes. In this paper, we propose a novel parametric level set method called Disjunctive Normal Level Set (DNLS), and apply it to both two phase (single object) and multiphase (multi-object) image segmentations. The DNLS is formed by union of polytopes which themselves are formed by intersections of half-spaces. The proposed level set framework has the following major advantages compared to other level set methods available in the literature. First, segmentation using DNLS converges much faster. Second, the DNLS level set function remains regular throughout its evolution. Third, the proposed multiphase version of the DNLS is less sensitive to initialization, and its computational cost and memory requirement remains almost constant as the number of objects to be simultaneously segmented grows. The experimental results show the potential of the proposed method.
Mutual exclusivity loss for semi-supervised deep learning|
M. Sajjadi, M. Javanmardi, T. Tasdizen. In 2016 IEEE International Conference on Image Processing (ICIP), IEEE, September, 2016.
In this paper we consider the problem of semi-supervised learning with deep Convolutional Neural Networks (ConvNets). Semi-supervised learning is motivated on the observation that unlabeled data is cheap and can be used to improve the accuracy of classifiers. In this paper we propose an unsupervised regularization term that explicitly forces the classifier's prediction for multiple classes to be mutually-exclusive and effectively guides the decision boundary to lie on the low density space between the manifolds corresponding to different classes of data. Our proposed approach is general and can be used with any backpropagation-based learning method. We show through different experiments that our method can improve the object recognition performance of ConvNets using unlabeled data.
|SSHMT: Semi-supervised Hierarchical Merge Tree for Electron Microscopy Image Segmentation,
T. Liu, M. Zhang, M. Javanmardi , N. Ramesh, T. Tasdizen. In Lecture Notes in Computer Science, Vol. 9905, Springer International Publishing, pp. 144--159. 2016.
Region-based methods have proven necessary for improving segmentation accuracy of neuronal structures in electron microscopy (EM) images. Most region-based segmentation methods use a scoring function to determine region merging. Such functions are usually learned with supervised algorithms that demand considerable ground truth data, which are costly to collect. We propose a semi-supervised approach that reduces this demand. Based on a merge tree structure, we develop a differentiable unsupervised loss term that enforces consistent predictions from the learned function. We then propose a Bayesian model that combines the supervised and the unsupervised information for probabilistic learning. The experimental results on three EM data sets demonstrate that by using a subset of only 3% to 7% of the entire ground truth data, our approach consistently performs close to the state-of-the-art supervised method with the full labeled data set, and significantly outperforms the supervised method with the same labeled subset.
Dendritic Spine Shape Analysis: A Clustering Perspective|
M.U. Ghani, E. Erdil, S.D. Kanik, A.O. Argunsah, A. Hobbiss, I. Israely, D. Unay, T. Tasdizen, M. Cetin. In Lecture Notes in Computer Science, Springer International Publishing, pp. 256--273. 2016.
Functional properties of neurons are strongly coupled with their morphology. Changes in neuronal activity alter morphological characteristics of dendritic spines. First step towards understanding the structure-function relationship is to group spines into main spine classes reported in the literature. Shape analysis of dendritic spines can help neuroscientists understand the underlying relationships. Due to unavailability of reliable automated tools, this analysis is currently performed manually which is a time-intensive and subjective task. Several studies on spine shape classification have been reported in the literature, however, there is an on-going debate on whether distinct spine shape classes exist or whether spines should be modeled through a continuum of shape variations. Another challenge is the subjectivity and bias that is introduced due to the supervised nature of classification approaches. In this paper, we aim to address these issues by presenting a clustering perspective. In this context, clustering may serve both confirmation of known patterns and discovery of new ones. We perform cluster analysis on two-photon microscopic images of spines using morphological, shape, and appearance based features and gain insights into the spine shape analysis problem. We use histogram of oriented gradients (HOG), disjunctive normal shape models (DNSM), morphological features, and intensity profile based features for cluster analysis. We use x-means to perform cluster analysis that selects the number of clusters automatically using the Bayesian information criterion (BIC). For all features, this analysis produces 4 clusters and we observe the formation of at least one cluster consisting of spines which are difficult to be assigned to a known class. This observation supports the argument of intermediate shape types.
Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning|
M. Sajjadi, M. Javanmardi, T. Tasdizen. In CoRR, Vol. abs/1606.04586, 2016.
Effective convolutional neural networks are trained on large sets of labeled data. However, creating large labeled datasets is a very costly and time-consuming task. Semi-supervised learning uses unlabeled data to train a model with higher accuracy when there is a limited set of labeled data available. In this paper, we consider the problem of semi-supervised learning with convolutional neural networks. Techniques such as randomized data augmentation, dropout and random max-pooling provide better generalization and stability for classifiers that are trained using gradient descent. Multiple passes of an individual sample through the network might lead to different predictions due to the non-deterministic behavior of these techniques. We propose an unsupervised loss function that takes advantage of the stochastic nature of these methods and minimizes the difference between the predictions of multiple passes of a training sample through the network. We evaluate the proposed method on several benchmark datasets.
Disjunctive Normal Networks|
M. Sajjadi, S.M. Seyedhosseini, T. Tasdizen. In Neurocomputing, Vol. 218, Elsevier BV, pp. 276--285. Dec, 2016.
Artificial neural networks are powerful pattern classifiers. They form the basis of the highly successful and popular Convolutional Networks which offer the state-of-the-art performance on several computer visions tasks. However, in many general and non-vision tasks, neural networks are surpassed by methods such as support vector machines and random forests that are also easier to use and faster to train. One reason is that the backpropagation algorithm, which is used to train artificial neural networks, usually starts from a random weight initialization which complicates the optimization process leading to long training times and increases the risk of stopping in a poor local minima. Several initialization schemes and pre-training methods have been proposed to improve the efficiency and performance of training a neural network. However, this problem arises from the architecture of neural networks. We use the disjunctive normal form and approximate the boolean conjunction operations with products to construct a novel network architecture. The proposed model can be trained by minimizing an error function and it allows an effective and intuitive initialization which avoids poor local minima. We show that the proposed structure provides efficient coverage of the decision space which leads to state-of-the art classification accuracy and fast training times.
Image Segmentation Using Hierarchical Merge Tree|
T. Liu, S.M. Seyedhosseini, T. Tasdizen. In IEEE Transactions on Image Processing, Vol. 25, No. 10, IEEE, pp. 4596--4607. Oct, 2016.
This paper investigates one of the most fundamental computer vision problems: image segmentation. We propose a supervised hierarchical approach to object-independent image segmentation. Starting with oversegmenting superpixels, we use a tree structure to represent the hierarchy of region merging, by which we reduce the problem of segmenting image regions to finding a set of label assignment to tree nodes. We formulate the tree structure as a constrained conditional model to associate region merging with likelihoods predicted using an ensemble boundary classifier. Final segmentations can then be inferred by finding globally optimal solutions to the model efficiently. We also present an iterative training and testing algorithm that generates various tree structures and combines them to emphasize accurate boundaries by segmentation accumulation. Experiment results and comparisons with other recent methods on six public datasets demonstrate that our approach achieves the state-of-the-art region accuracy and is competitive in image segmentation without semantic priors.