Diffusion Tensor Analysis
Clustering With Pairwise Relationships: A Generative Approach|
Y.Y. Yu, S.Y. Elhabian, R.T. Whitaker. In CoRR, 2018.
Semi-supervised learning (SSL) has become important in current data analysis applications, where the amount of unlabeled data is growing exponentially and user input remains limited by logistics and expense. Constrained clustering, as a subclass of SSL, makes use of user input in the form of relationships between data points (e.g., pairs of data points belonging to the same class or different classes) and can remarkably improve the performance of unsupervised clustering in order to reflect user-defined knowledge of the relationships between particular data points. Existing algorithms incorporate such user input, heuristically, as either hard constraints or soft penalties, which are separate from any generative or statistical aspect of the clustering model; this results in formulations that are suboptimal and not sufficiently general. In this paper, we propose a principled, generative approach to probabilistically model, without ad hoc penalties, the joint distribution given by user-defined pairwise relations. The proposed model accounts for general underlying distributions without assuming a specific form and relies on expectation-maximization for model fitting. For distributions in a standard form, the proposed approach results in a closed-form solution for updated parameters.
Latent Space Non-Linear Statistics|
L. Kuhnel, T. Fletcher, S. Joshi, S. Sommer. In CoRR, 2018.
Given data, deep generative models, such as variational autoencoders (VAE) and generative adversarial networks (GAN), train a lower dimensional latent representation of the data space. The linear Euclidean geometry of data space pulls back to a nonlinear Riemannian geometry on the latent space. The latent space thus provides a low-dimensional nonlinear representation of data and classical linear statistical techniques are no longer applicable. In this paper we show how statistics of data in their latent space representation can be performed using techniques from the field of nonlinear manifold statistics. Nonlinear manifold statistics provide generalizations of Euclidean statistical notions including means, principal component analysis, and maximum likelihood fits of parametric probability distributions. We develop new techniques for maximum likelihood inference in latent space, and adress the computational complexity of using geometric algorithms with high-dimensional data by training a separate neural network to approximate the Riemannian metric and cometric tensor capturing the shape of the learned data manifold.
Skeletal Shape Correspondence through Entropy|
L. Tu, M. Styner, J. Vicory, S. Elhabian, R. Wang, J. Hong, B. Paniagua, J.C. Prieto, D. Yang, R. Whitaker, M. Pizer. In IEEE Transactions on Medical Imaging, Vol. 37, No. 1, IEEE, pp. 1--11. Jan, 2018.
We present a novel approach for improving the shape statistics of medical image objects by generating correspondence of skeletal points. Each object's interior is modeled by an s-rep, i.e., by a sampled, folded, two-sided skeletal sheet with spoke vectors proceeding from the skeletal sheet to the boundary. The skeleton is divided into three parts: the up side, the down side, and the fold curve. The spokes on each part are treated separately and, using spoke interpolation, are shifted along that skeleton in each training sample so as to tighten the probability distribution on those spokes' geometric properties while sampling the object interior regularly. As with the surface/boundary-based correspondence method of Cates et al., entropy is used to measure both the probability distribution tightness and the sampling regularity, here of the spokes' geometric properties. Evaluation on synthetic and real world lateral ventricle and hippocampus data sets demonstrate improvement in the performance of statistics using the resulting probability distributions. This improvement is greater than that achieved by an entropy-based correspondence method on the boundary points.
F. Mesadi, E. Erdil, M. Cetin, T. Tasdizen|
Image segmentation using disjunctive normal Bayesian shape, appearance models. In IEEE Transactions on Medical Imaging, Vol. 37, No. 1, IEEE, pp. 293--305. Jan, 2018.
The use of appearance and shape priors in image segmentation is known to improve accuracy; however, existing techniques have several drawbacks. For instance, most active shape and appearance models require landmark points and assume unimodal shape and appearance distributions, and the level set representation does not support construction of local priors. In this paper, we present novel appearance and shape models for image segmentation based on a differentiable implicit parametric shape representation called a disjunctive normal shape model (DNSM). The DNSM is formed by the disjunction of polytopes, which themselves are formed by the conjunctions of half-spaces. The DNSM's parametric nature allows the use of powerful local prior statistics, and its implicit nature removes the need to use landmarks and easily handles topological changes. In a Bayesian inference framework, we model arbitrary shape and appearance distributions using nonparametric density estimations, at any local scale. The proposed local shape prior results in accurate segmentation even when very few training shapes are available, because the method generates a rich set of shape variations by locally combining training samples. We demonstrate the performance of the framework by applying it to both 2-D and 3-D data sets with emphasis on biomedical image segmentation applications.
A virtual reality visualization tool for neuron tracing|
W Usher, P Klacansky, F Federer, PT Bremer, A Knoll, J. Yarch, A. Angelucci, V. Pascucci . In IEEE Transactions on Visualization and Computer Graphics, Vol. 24, No. 1, IEEE, pp. 994--1003. Jan, 2018.
racing neurons in large-scale microscopy data is crucial to establishing a wiring diagram of the brain, which is needed to understand how neural circuits in the brain process information and generate behavior. Automatic techniques often fail for large and complex datasets, and connectomics researchers may spend weeks or months manually tracing neurons using 2D image stacks. We present a design study of a new virtual reality (VR) system, developed in collaboration with trained neuroanatomists, to trace neurons in microscope scans of the visual cortex of primates. We hypothesize that using consumer-grade VR technology to interact with neurons directly in 3D will help neuroscientists better resolve complex cases and enable them to trace neurons faster and with less physical and mental strain. We discuss both the design process and technical challenges in developing an interactive system to navigate and manipulate terabyte-sized image volumes in VR. Using a number of different datasets, we demonstrate that, compared to widely used commercial software, consumer-grade VR presents a promising alternative for scientists.
Neighbourhood looking glass: 360º automated characterisation of the built environment for neighbourhood effects research|
Q.C. Nguyen, M. Sajjadi, M. McCullough, M. Pham, T.T. Nguyen, W. Yu, H. Meng, M. Wen, F. Li, K.R. Smith, K. Brunisholz, T, Tasdizen. In Journal of Epidemiology and Community Health, BMJ, Jan, 2018.
Image reconstruction using priors from deep learning|
D. Ayyagari, N. Ramesh, D. Yatsenko, T. Tasdizen, C. Atria. In Medical Imaging 2018: Image Processing, SPIE, March, 2018.
Tomosynthesis, i.e. reconstruction of 3D volumes using projections from a limited perspective is a classical inverse, ill-posed or under constrained problem. Data insufficiency leads to reconstruction artifacts that vary in severity depending on the particular problem, the reconstruction method and also on the object being imaged. Machine learning has been used successfully in tomographic problems where data is insufficient, but the challenge with machine learning is that it introduces bias from the learning dataset. A novel framework to improve the quality of the tomosynthesis reconstruction that limits the learning dataset bias by maintaining consistency with the observed data is proposed. Convolutional Neural Networks (CNN) are embedded as regularizers in the reconstruction process to introduce the expected features and characterstics of the likely imaged object. The minimization of the objective function keeps the solution consistent with the observations and limits the bias introduced by the machine learning regularizers, improving the quality of the reconstruction. The proposed method has been developed and studied in the specific problem of Cone Beam Tomosynthesis Flouroscopy (CBT-fluoroscopy)1 but it is a general framework that can be applied to any image reconstruction problem that is limited by data insufficiency.
Domain adaptation for biomedical image segmentation using adversarial training|
M. Javanmardi, T. Tasdizen. In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), IEEE, pp. 554-558. April, 2018.
Many biomedical image analysis applications require segmentation. Convolutional neural networks (CNN) have become a promising approach to segment biomedical images; however, the accuracy of these methods is highly dependent on the training data. We focus on biomedical image segmentation in the context where there is variation between source and target datasets and ground truth for the target dataset is very limited or non-existent. We use an adversarial based training approach to train CNNs to achieve good accuracy on the target domain. We use the DRIVE and STARE eye vasculture segmentation datasets and show that our approach can significantly improve results where we only use labels of one domain in training and test on the other domain. We also show improvements on membrane detection between MIC-CAI 2016 CREMI challenge and ISBI2013 EM segmentation challenge datasets.
Semi-supervised learning for cell tracking in microscopy images|
N. Ramesh, T. Tasdizen. In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), IEEE, April, 2018.
This paper discusses an algorithm for semi-supervised learning to predict cell division and motion in microscopy images. The cells for tracking are detected using extremal region selection and are depicted using a graphical representation. The supervised loss minimizes the error in predictions for the division and move classifiers. The unsupervised loss constrains the incoming links for every detection such that only one of the links is active. Similarly for the outgoing links, we enforce at-most two links to be active. The supervised and un-supervised losses are embedded in a Bayesian framework for probabilistic learning. The classifier predictions are used to model flow variables for every edge in the graph. The cell lineages are solved by formulating it as an energy minimization problem with constraints using integer linear programming. The unsupervised loss adds a significant improvement in the prediction of the division classifier.
Improving the robustness of convolutional networks to appearance variability in biomedical images|
T. Tasdizen, M. Sajjadi, M. Javanmardi, N. Ramesh. In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), IEEE, April, 2018.
While convolutional neural networks (CNN) produce state-of-the-art results in many applications including biomedical image analysis, they are not robust to variability in the data that is not well represented by the training set. An important source of variability in biomedical images is the appearance of objects such as contrast and texture due to different imaging settings. We introduce the neighborhood similarity layer (NSL) which can be used in a CNN to improve robustness to changes in the appearance of objects that are not well represented by the training data. The proposed NSL transforms its input feature map at a given pixel by computing its similarity to the surrounding neighborhood. This transformation is spatially varying, hence not a convolution. It is differentiable; therefore, networks including the proposed layer can be trained in an end-to-end manner. We demonstrate the advantages of the NSL for the vasculature segmentation and cell detection problems.
High resolution and high field diffusion MRI in the visual system of primates (P3.086)|
O. Abdullah, L. Dai, J. Tippetts, B. Zimmerman, A. Van Hoek, S. Joshi, E. Hsu. In Neurology, Vol. 90, No. 15 Supplement, Wolters Kluwer Health, Inc, 2018.
Objective: Establishing a primate multiscale genetic brain network linking key microstructural brain components to social behavior remains an elusive goal.
Real-Time Patient-Specific Lung Radiotherapy Targeting using Deep Learning|
M.D. Foote, B. Zimmerman, A. Sawant, S. Joshi. In 1st Conference on Medical Imaging with Deep Learning (MIDL 2018), Amsterdam, The Netherlands, 2018.
Radiation therapy has presented a need for dynamic tracking of a target tumor volume. Fiducial markers such as implanted gold seeds have been used to gate radiation delivery but the markers are invasive and gating significantly increases treatment time. Pretreatment acquisition of a 4DCT allows for the development of accurate motion estimation for treatment planning. A deep convolutional neural network and subspace motion tracking is used to recover anatomical positions from a single radiograph projection in real-time. We approximate the nonlinear inverse of a diffeomorphic transformation composed with radiographic projection as a deep network that produces subspace coordinates to define the patient-specific deformation of the lungs from a baseline anatomic position. The geometric accuracy of the subspace projections on real patient data is similar to accuracy attained by original image registration between individual respiratory-phase image volumes.
Flexible Live‐Wire: Image Segmentation with Floating Anchors|
B. Summa, N. Faraj, C. Licorish, V. Pascucci. In Computer Graphics Forum, Vol. 37, No. 2, Wiley, pp. 321-328. May, 2018.
We introduce Flexible Live‐Wire, a generalization of the Live‐Wire interactive segmentation technique with floating anchors. In our approach, the user input for Live‐Wire is no longer limited to the setting of pixel‐level anchor nodes, but can use more general anchor sets. These sets can be of any dimension, size, or connectedness. The generality of the approach allows the design of a number of user interactions while providing the same functionality as the traditional Live‐Wire. In particular, we experiment with this new flexibility by designing four novel Live‐Wire interactions based on specific primitives: paint, pinch, probable, and pick anchors. These interactions are only a subset of the possibilities enabled by our generalization. Moreover, we discuss the computational aspects of this approach and provide practical solutions to alleviate any additional overhead. Finally, we illustrate our approach and new interactions through several example segmentations.
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.
|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.