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.
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.