Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.
Deep brain stimulation
BrainStimulator is a set of networks that are used in SCIRun to perform simulations of brain stimulation such as transcranial direct current stimulation (tDCS) and magnetic transcranial stimulation (TMS).
Developing software tools for science has always been a central vision of the SCI Institute.

Image Analysis

SCI's imaging work addresses fundamental questions in 2D and 3D image processing, including filtering, segmentation, surface reconstruction, and shape analysis. In low-level image processing, this effort has produce new nonparametric methods for modeling image statistics, which have resulted in better algorithms for denoising and reconstruction. Work with particle systems has led to new methods for visualizing and analyzing 3D surfaces. Our work in image processing also includes applications of advanced computing to 3D images, which has resulted in new parallel algorithms and real-time implementations on graphics processing units (GPUs). Application areas include medical image analysis, biological image processing, defense, environmental monitoring, and oil and gas.


ross

Ross Whitaker

Segmentation
sarang

Sarang Joshi

Shape Statistics
Segmentation
Brain Atlasing
tolga

Tolga Tasdizen

Image Processing
Machine Learning
chris

Chris Johnson

Diffusion Tensor Analysis
shireen

Shireen Elhabian

Image Analysis
Computer Vision


Funded Research Projects:



Publications in Image Analysis:


Prediction of Femoral Head Coverage from Articulated Statistical Shape Models of Patients with Developmental Dysplasia of the Hip
P. R. Atkins, P. Agrawal, J. D. Mozingo, K. Uemura, K. Tokunaga, C. L. Peters, S. Y. Elhabian, R. T. Whitaker, A. E. Anderson. In Journal of Orthopaedic Research, Wiley, 2021.
DOI: 10.1002/jor.25227

Developmental dysplasia of the hip (DDH) is commonly described as reduced femoral head coverage due to anterolateral acetabular deficiency. Although reduced coverage is the defining trait of DDH, more subtle and localized anatomic features of the joint are also thought to contribute to symptom development and degeneration. These features are challenging to identify using conventional approaches. Herein, we assessed the morphology of the full femur and hemi-pelvis using an articulated statistical shape model (SSM). The model determined the morphological and pose-based variations associated with DDH in a population of Japanese females and established which of these variations predict coverage. Computed tomography images of 83 hips from 47 patients were segmented for input into a correspondence-based SSM. The dominant modes of variation in the model initially represented scale and pose. After removal of these factors through individual bone alignment, femoral version and neck-shaft angle, pelvic curvature, and acetabular version dominated the observed variation. Femoral head oblateness and prominence of the acetabular rim and various muscle attachment sites of the femur and hemi-pelvis were found to predict 3D CT-based coverage measurements (R2=0.5-0.7 for the full bones, R2=0.9 for the joint).



Validation of Artificial Intelligence Severity Assessment in Metopic Craniosynostosis
A. Junn, J. Dinis, S. C. Hauc, M. K. Bruce, K. E. Park, W. Tao, C. Christensen, R. Whitaker, J. A. Goldstein, M. Alperovich. In The Cleft Palate-Craniofacial Journal, SAGE Publications, 2021.
DOI: https://doi.org/10.1177/10.1177/10556656211061021

Objective
Several severity metrics have been developed for metopic craniosynostosis, including a recent machine learning-derived algorithm. This study assessed the diagnostic concordance between machine learning and previously published severity indices.

Design
Preoperative computed tomography (CT) scans of patients who underwent surgical correction of metopic craniosynostosis were quantitatively analyzed for severity. Each scan was manually measured to derive manual severity scores and also received a scaled metopic severity score (MSS) assigned by the machine learning algorithm. Regression analysis was used to correlate manually captured measurements to MSS. ROC analysis was performed for each severity metric and were compared to how accurately they distinguished cases of metopic synostosis from controls.
Results
In total, 194 CT scans were analyzed, 167 with metopic synostosis and 27 controls. The mean scaled MSS for the patients with metopic was 6.18 ± 2.53 compared to 0.60 ± 1.25 for controls. Multivariable regression analyses yielded an R-square of 0.66, with significant manual measurements of endocranial bifrontal angle (EBA) (P = 0.023), posterior angle of the anterior cranial fossa (p < 0.001), temporal depression angle (P = 0.042), age (P < 0.001), biparietal distance (P < 0.001), interdacryon distance (P = 0.033), and orbital width (P < 0.001). ROC analysis demonstrated a high diagnostic value of the MSS (AUC = 0.96, P < 0.001), which was comparable to other validated indices including the adjusted EBA (AUC = 0.98), EBA (AUC = 0.97), and biparietal/bitemporal ratio (AUC = 0.95).
Conclusions
The machine learning algorithm offers an objective assessment of morphologic severity that provides a reliable composite impression of severity. The generated score is comparable to other severity indices in ability to distinguish cases of metopic synostosis from controls.



Determining the Composition of a Mixed Material with Synthetic Data
C. Ly, C. A. Nizinski, A. Toydemir, C. Vachet, L. W. McDonald, T. Tasdizen. In Microscopy and Microanalysis, Cambridge University Press, pp. 1--11. 2021.
DOI: 10.1017/S1431927621012915

Determining the composition of a mixed material is an open problem that has attracted the interest of researchers in many fields. In our recent work, we proposed a novel approach to determine the composition of a mixed material using convolutional neural networks (CNNs). In machine learning, a model “learns” a specific task for which it is designed through data. Hence, obtaining a dataset of mixed materials is required to develop CNNs for the task of estimating the composition. However, the proposed method instead creates the synthetic data of mixed materials generated from using only images of pure materials present in those mixtures. Thus, it eliminates the prohibitive cost and tedious process of collecting images of mixed materials. The motivation for this study is to provide mathematical details of the proposed approach in addition to extensive experiments and analyses. We examine the approach on two datasets to demonstrate the ease of extending the proposed approach to any mixtures. We perform experiments to demonstrate that the proposed approach can accurately determine the presence of the materials, and sufficiently estimate the precise composition of a mixed material. Moreover, we provide analyses to strengthen the validation and benefits of the proposed approach.



Computational Image Techniques for Analyzing Lanthanide and Actinide Morphology,
C. A. Nizinski, C. Ly, L. W. McDonald IV, T. Tasdizen. In Rare Earth Elements and Actinides: Progress in Computational Science Applications, Ch. 6, pp. 133-155. 2021.
DOI: 10.1021/bk-2021-1388.ch006

This chapter introduces computational image analysis techniques and how they may be used for material characterization as it pertains to lanthanide and actinide chemistry. Specifically, the underlying theory behind particle segmentation, texture analysis, and convolutional neural networks for material characterization are briefly summarized. The variety of particle segmentation techniques that have been used to effectively measure the size and shape of morphological features from scanning electron microscope images will be discussed. In addition, the extraction of image texture features via gray-level co-occurrence matrices and angle measurement techniques are described and demonstrated. To conclude, the application of convolutional neural networks to lanthanide and actinide materials science challenges are described with applications for image classification, feature extraction, and predicting a materials morphology discussed.



DeepSSM: A Blueprint for Image-to-Shape Deep Learning Models
Subtitled “arXiv preprint arXiv:2110.07152,” R. Bhalodia, S. Elhabian, J. Adams, W. Tao, L. Kavan, R. Whitaker. 2021.

Statistical shape modeling (SSM) characterizes anatomical variations in a population of shapes generated from medical images. SSM requires consistent shape representation across samples in shape cohort. Establishing this representation entails a processing pipeline that includes anatomy segmentation, re-sampling, registration, and non-linear optimization. These shape representations are then used to extract low-dimensional shape descriptors that facilitate subsequent analyses in different applications. However, the current process of obtaining these shape descriptors from imaging data relies on human and computational resources, requiring domain expertise for segmenting anatomies of interest. Moreover, this same taxing pipeline needs to be repeated to infer shape descriptors for new image data using a pre-trained/existing shape model. Here, we propose DeepSSM, a deep learning-based framework for learning the functional mapping from images to low-dimensional shape descriptors and their associated shape representations, thereby inferring statistical representation of anatomy directly from 3D images. Once trained using an existing shape model, DeepSSM circumvents the heavy and manual pre-processing and segmentation and significantly improves the computational time, making it a viable solution for fully end-to-end SSM applications. In addition, we introduce a model-based data-augmentation strategy to address data scarcity. Finally, this paper presents and analyzes two different architectural variants of DeepSSM with different loss functions using three medical datasets and their downstream clinical application. Experiments showcase that DeepSSM performs comparably or better to the state-of-the-art SSM both quantitatively and on application-driven downstream tasks. Therefore, DeepSSM aims to provide a comprehensive blueprint for deep learning-based image-to-shape models.



REFLACX, a dataset of reports and eye-tracking data for localization of abnormalities in chest x-rays
Subtitled “arXiv:2109.14187,” R. B. Lanfredi, M. Zhang, W. F. Auffermann, J. Chan, P. T. Duong, V. Srikumar, T. Drew, J. D. Schroeder, T. Tasdizen. 2021.

Deep learning has shown recent success in classifying anomalies in chest x-rays, but datasets are still small compared to natural image datasets. Supervision of abnormality localization has been shown to improve trained models, partially compensating for dataset sizes. However, explicitly labeling these anomalies requires an expert and is very time-consuming. We propose a method for collecting implicit localization data using an eye tracker to capture gaze locations and a microphone to capture a dictation of a report, imitating the setup of a reading room, and potentially scalable for large datasets. The resulting REFLACX (Reports and Eye-Tracking Data for Localization of Abnormalities in Chest X-rays) dataset was labeled by five radiologists and contains 3,032 synchronized sets of eye-tracking data and timestamped report transcriptions. We also provide bounding boxes around lungs and heart and validation labels consisting of ellipses localizing abnormalities and image-level labels. Furthermore, a small subset of the data contains readings from all radiologists, allowing for the calculation of inter-rater scores.



A Gaussian Process Model for Unsupervised Analysis of High Dimensional Shape Data,
W. Tao, R. Bhalodia, R. Whitaker. In Machine Learning in Medical Imaging, Springer International Publishing, pp. 356--365. 2021.
DOI: 10.1007/978-3-030-87589-3_37

Applications of medical image analysis are often faced with the challenge of modelling high-dimensional data with relatively few samples. In many settings, normal or healthy samples are prevalent while pathological samples are rarer, highly diverse, and/or difficult to model. In such cases, a robust model of the normal population in the high-dimensional space can be useful for characterizing pathologies. In this context, there is utility in hybrid models, such as probabilistic PCA, which learns a low-dimensional model, commensurates with the available data, and combines it with a generic, isotropic noise model for the remaining dimensions. However, the isotropic noise model ignores the inherent correlations that are evident in so many high-dimensional data sets associated with images and shapes in medicine. This paper describes a method for estimating a Gaussian model for collections of images or shapes that exhibit underlying correlations, e.g., in the form of smoothness. The proposed method incorporates a Gaussian-process noise model within a generative formulation. For optimization, we derive a novel expectation maximization (EM) algorithm. We demonstrate the efficacy of the method on synthetic examples and on anatomical shape data.



A Nonparametric Approach for Estimating Three-Dimensional Fiber Orientation Distribution Functions (ODFs) in Fibrous Materials
A. Rauff, L.H. Timmins, R.T. Whitaker, J.A. Weiss. In IEEE Transactions on Medical Imaging, 2021.
DOI: 10.1109/TMI.2021.3115716

Many biological tissues contain an underlying fibrous microstructure that is optimized to suit a physiological function. The fiber architecture dictates physical characteristics such as stiffness, diffusivity, and electrical conduction. Abnormal deviations of fiber architecture are often associated with disease. Thus, it is useful to characterize fiber network organization from image data in order to better understand pathological mechanisms. We devised a method to quantify distributions of fiber orientations based on the Fourier transform and the Qball algorithm from diffusion MRI. The Fourier transform was used to decompose images into directional components, while the Qball algorithm efficiently converted the directional data from the frequency domain to the orientation domain. The representation in the orientation domain does not require any particular functional representation, and thus the method is nonparametric. The algorithm was verified to demonstrate its reliability and used on datasets from microscopy to show its applicability. This method increases the ability to extract information of microstructural fiber organization from experimental data that will enhance our understanding of structure-function relationships and enable accurate representation of material anisotropy in biological tissues.



Integrated Construction of Multimodal Atlases with Structural Connectomes in the Space of Riemannian Metrics
Subtitled “arXiv preprint arXiv:2109.09808,” K.M. Campbell, H. Dai, Z. Su, M. Bauer, P.T. Fletcher, S.C. Joshi. 2021.

The structural network of the brain, or structural connectome, can be represented by fiber bundles generated by a variety of tractography methods. While such methods give qualitative insights into brain structure, there is controversy over whether they can provide quantitative information, especially at the population level. In order to enable population-level statistical analysis of the structural connectome, we propose representing a connectome as a Riemannian metric, which is a point on an infinite-dimensional manifold. We equip this manifold with the Ebin metric, a natural metric structure for this space, to get a Riemannian manifold along with its associated geometric properties. We then use this Riemannian framework to apply object-oriented statistical analysis to define an atlas as the Fr\'echet mean of a population of Riemannian metrics. This formulation ties into the existing framework for diffeomorphic construction of image atlases, allowing us to construct a multimodal atlas by simultaneously integrating complementary white matter structure details from DWMRI and cortical details from T1-weighted MRI. We illustrate our framework with 2D data examples of connectome registration and atlas formation. Finally, we build an example 3D multimodal atlas using T1 images and connectomes derived from diffusion tensors estimated from a subset of subjects from the Human Connectome Project.



Learning to Estimate the Composition of a Mixture with Synthetic Data
C. Ly, C. Nizinski, C. Vachet, L. McDonald, T. Tasdizen. In Microscopy and Microanalysis, 2021.

Identifying the precise composition of a mixed material is important in various applications. For instance, in nuclear forensics analysis, knowing the process history of unknown or illicitly trafficked nuclear materials when they are discovered is desirable to prevent future losses or theft of material from the processing facilities. Motivated by this open problem, we describe a novel machine learning approach to determine the composition of a mixture from SEM images. In machine learning, the training data distribution should reflect the distribution of the data the model is expected to make predictions for, which can pose a hurdle. However, a key advantage of our proposed framework is that it requires reference images of pure material samples only. Removing the need for reference samples of various mixed material compositions reduces the time and monetary cost associated with reference sample preparation and imaging. Moreover, our proposed framework can determine the composition of a mixture composed of chemically similar materials, whereas other elemental analysis tools such as powder X-ray diffraction (p-XRD) have trouble doing so. For example, p-XRD is unable to discern mixtures composed of triuranium octoxide (U3O8) synthesized from different synthetic routes such as uranyl peroxide (UO4) and ammonium diuranate (ADU) [1]. In contrast, our proposed framework can easily determine the composition of uranium oxides mixture synthesized from different synthetic routes, as we illustrate in the experiments.



Leveraging Unsupervised Image Registration for Discovery of Landmark Shape Descriptor
R. Bhalodia, S. Elhabian, L. Kavan, R. Whitaker. In Medical Image Analysis, Elsevier, pp. 102157. 2021.

In current biological and medical research, statistical shape modeling (SSM) provides an essential framework for the characterization of anatomy/morphology. Such analysis is often driven by the identification of a relatively small number of geometrically consistent features found across the samples of a population. These features can subsequently provide information about the population shape variation. Dense correspondence models can provide ease of computation and yield an interpretable low-dimensional shape descriptor when followed by dimensionality reduction. However, automatic methods for obtaining such correspondences usually require image segmentation followed by significant preprocessing, which is taxing in terms of both computation as well as human resources. In many cases, the segmentation and subsequent processing require manual guidance and anatomy specific domain expertise. This paper proposes a self-supervised deep learning approach for discovering landmarks from images that can directly be used as a shape descriptor for subsequent analysis. We use landmark-driven image registration as the primary task to force the neural network to discover landmarks that register the images well. We also propose a regularization term that allows for robust optimization of the neural network and ensures that the landmarks uniformly span the image domain. The proposed method circumvents segmentation and preprocessing and directly produces a usable shape descriptor using just 2D or 3D images. In addition, we also propose two variants on the training loss function that allows for prior shape information to be integrated into the model. We apply this framework on several 2D and 3D datasets to obtain their shape descriptors. We analyze these shape descriptors in their efficacy of capturing shape information by performing different shape-driven applications depending on the data ranging from shape clustering to severity prediction to outcome diagnosis.



Impact of scene-specific enhancement spectra on matched filter greenhouse gas retrievals from imaging spectroscopy
M. D. Foote, P. E. Dennison, P. R. Sullivan, K. B. O'Neill, A. K. Thorpe, D. R. Thompson, D. H. Cusworth, R. Duren, S. Joshi. In Remote Sensing of Environment, Vol. 264, Elsevier, pp. 112574. 2021.

Matched filter techniques have been widely used for retrieval of greenhouse gas enhancements from imaging spectroscopy datasets. While multiple algorithmic techniques and refinements have been proposed, the greenhouse gas target spectrum used for concentration enhancement estimation has remained largely unaltered since the introduction of quantitative matched filter retrievals. The magnitude of retrieved methane and carbon dioxide enhancements, and thereby integrated mass enhancements (IME) and estimated flux of point-source emitters, is heavily dependent on this target spectrum. Current standard use of molecular absorption coefficients to create unit enhancement target spectra does not account for absorption by background concentrations of greenhouse gases, solar and sensor geometry, or atmospheric water vapor absorption. We introduce geometric and atmospheric parameters into the generation of scene-specific unit enhancement spectra to provide target spectra that are compatible with all greenhouse gas retrieval matched filter techniques. Specifically, we use radiative transfer modeling to model four parameters that are expected to change between scenes: solar zenith angle, column water vapor, ground elevation, and sensor altitude. These parameter values are well defined, with low variation within a single scene. A benchmark dataset consisting of ten AVIRIS-NG airborne imaging spectrometer scenes was used to compare IME retrieved using a matched filter algorithm. For methane plumes, IME resulting from use of standard, generic enhancement spectra varied from −22 to +28.7% compared to scene-specific enhancement spectra. Due to differences in spectral shape between the generic and scene-specific enhancement spectra, differences in methane plume IME were linked to surface spectral characteristics in addition to geometric and atmospheric parameters. IME differences were much larger for carbon dioxide plumes, with generic enhancement spectra producing integrated mass enhancements −76.1 to −48.1% compared to scene-specific enhancement spectra. Fluxes calculated from these integrated enhancements would vary by the same percentages, assuming equivalent wind conditions. Methane and carbon dioxide IME were most sensitive to changes in solar zenith angle and ground elevation. We introduce an interpolation approach that can efficiently generate scene-specific unit enhancement spectra for given sets of parameters. Scene-specific target spectra can improve confidence in greenhouse gas retrievals and flux estimates across collections of scenes with diverse geometric and atmospheric conditions.



Bridge Simulation and Metric Estimation on Lie Groups
Subtitled “arXiv preprint arXiv:2106.03431,” M. H. Jensen, S. Joshi, S. Sommer. 2021.

We present a simulation scheme for simulating Brownian bridges on complete and connected Lie groups. We show how this simulation scheme leads to absolute continuity of the Brownian bridge measure with respect to the guided process measure. This result generalizes the Euclidean result of Delyon and Hu to Lie groups. We present numerical results of the guided process in the Lie group $\SO(3)$. In particular, we apply importance sampling to estimate the metric on $\SO(3)$ using an iterative maximum likelihood method.



Interactive Analysis for Large Volume Data from Fluorescence Microscopy at Cellular Precision
Y. Wan, H.A. Holman, C. Hansen. In Computers & Graphics, Vol. 98, Pergamon, pp. 138-149. 2021.
DOI: https://doi.org/10.1016/j.cag.2021.05.006

The main objective for understanding fluorescence microscopy data is to investigate and evaluate the fluorescent signal intensity distributions as well as their spatial relationships across multiple channels. The quantitative analysis of 3D fluorescence microscopy data needs interactive tools for researchers to select and focus on relevant biological structures. We developed an interactive tool based on volume visualization techniques and GPU computing for streamlining rapid data analysis. Our main contribution is the implementation of common data quantification functions on streamed volumes, providing interactive analyses on large data without lengthy preprocessing. Data segmentation and quantification are coupled with brushing and executed at an interactive speed. A large volume is partitioned into data bricks, and only user-selected structures are analyzed to constrain the computational load. We designed a framework to assemble a sequence of GPU programs to handle brick borders and stitch analysis results. Our tool was developed in collaboration with domain experts and has been used to identify cell types. We demonstrate a workflow to analyze cells in vestibular epithelia of transgenic mice.



Loon: Using Exemplars to Visualize Large Scale Microscopy Data
D. Lange, E. Polanco, R. Judson-Torres, T. Zangle, A. Lex. In OSF Preprints, 2021.

Which drug is most promising for a cancer patient? This is a question a new microscopy-based approach for measuring the mass of individual cancer cells treated with different drugs promises to answer in only a few hours. However, the analysis pipeline for extracting data from these images is still far from complete automation: human intervention is necessary for quality control for preprocessing steps such as segmentation, to adjust filters, and remove noise, and for the analysis of the result. To address this workflow, we developed Loon, a visualization tool for analyzing drug screening data based on quantitative phase microscopy imaging. Loon visualizes both, derived data such as growth rates, and imaging data. Since the images are collected automatically at a large scale, manual inspection of images and segmentations is infeasible. However, reviewing representative samples of cells is essential, both for quality control and for data analysis. We introduce a new approach of choosing and visualizing representative exemplar cells that retain a close connection to the low-level data. By tightly integrating the derived data visualization capabilities with the novel exemplar visualization and providing selection and filtering capabilities, Loon is well suited for making decisions about which drugs are suitable for a specific patient.



Small-molecule mimicry hunting strategy in the imperial cone snail, Conus imperialis
J. P. Torres, Z. Lin, M. Watkins, P. F. Salcedo, R. P. Baskin, S. Elhabian, H. Safavi-Hemami, D. Taylor, J. Tun, G. P. Concepcion, N. Saguil, A. A. Yanagihara, Y. Fang, J. R. McArthur, H. Tae, R. K. Finol-Urdaneta, B. D. Özpolat, B. M. Olivera, E. W. Schmidt. In Science Advances, Vol. 7, No. 11, American Association for the Advancement of Science, 2021.

Venomous animals hunt using bioactive peptides, but relatively little is known about venom small molecules and the resulting complex hunting behaviors. Here, we explored the specialized metabolites from the venom of the worm-hunting cone snail, Conus imperialis. Using the model polychaete worm Platynereis dumerilii, we demonstrate that C. imperialis venom contains small molecules that mimic natural polychaete mating pheromones, evoking the mating phenotype in worms. The specialized metabolites from different cone snails are species-specific and structurally diverse, suggesting that the cones may adopt many different prey-hunting strategies enabled by small molecules. Predators sometimes attract prey using the prey’s own pheromones, in a strategy known as aggressive mimicry. Instead, C. imperialis uses metabolically stable mimics of those pheromones, indicating that, in biological mimicry, even the molecules themselves may be disguised, providing a twist on fake news in chemical ecology.



Learning Deep Features for Shape Correspondence with Domain Invariance
Subtitled “arXiv preprint arXiv:2102.10493,” P. Agrawal, R. T. Whitaker, S. Y. Elhabian. 2021.

Correspondence-based shape models are key to various medical imaging applications that rely on a statistical analysis of anatomies. Such shape models are expected to represent consistent anatomical features across the population for population-specific shape statistics. Early approaches for correspondence placement rely on nearest neighbor search for simpler anatomies. Coordinate transformations for shape correspondence hold promise to address the increasing anatomical complexities. Nonetheless, due to the inherent shape-level geometric complexity and population-level shape variation, the coordinate-wise correspondence often does not translate to the anatomical correspondence. An alternative, group-wise approach for correspondence placement explicitly models the trade-off between geometric description and the population's statistical compactness. However, these models achieve limited success in resolving nonlinear shape correspondence. Recent works have addressed this limitation by adopting an application-specific notion of correspondence through lifting positional data to a higher dimensional feature space. However, they heavily rely on manual expertise to create domain-specific features and consistent landmarks. This paper proposes an automated feature learning approach, using deep convolutional neural networks to extract correspondence-friendly features from shape ensembles. Further, an unsupervised domain adaptation scheme is introduced to augment the pretrained geometric features with new anatomies. Results on anatomical datasets of human scapula, femur, and pelvis bones demonstrate that …



Image-Based Multiresolution Topology Optimization Using Deep Disjunctive Normal Shape Model
V. Keshavarzzadeh, M. Alirezaei, T. Tasdizen, R. M. Kirby. In Computer-Aided Design, Vol. 130, Elsevier, pp. 102947. 2021.

We present a machine learning framework for predicting the optimized structural topology design susing multiresolution data. Our approach primarily uses optimized designs from inexpensive coarse mesh finite element simulations for model training and generates high resolution images associated with simulation parameters that are not previously used. Our cost-efficient approach enables the designers to effectively search through possible candidate designs in situations where the design requirements rapidly change. The underlying neural network framework is based on a deep disjunctive normal shape model (DDNSM) which learns the mapping between the simulation parameters and segments of multi resolution images. Using this image-based analysis we provide a practical algorithm which enhances the predictability of the learning machine by determining a limited number of important parametric samples(i.e.samples of the simulation parameters)on which the high resolution training data is generated. We demonstrate our approach on benchmark compliance minimization problems including the 3D topology optimization where we show that the high-fidelity designs from the learning machine are close to optimal designs and can be used as effective initial guesses for the large-scale optimization problem.



Detection and segmentation in microscopy images,
N. Ramesh, T. Tasdizen. In Computer Vision for Microscopy Image Analysis, Academic Press, pp. 43-71. 2021.
DOI: 10.1016/B978-0-12-814972-0.00003-5

The plethora of heterogeneous data generated using modern microscopy imaging techniques eliminates the possibility of manual image analysis for biologists. Consequently, reliable and robust computerized techniques are critical to analyze microscopy data. Detection problems in microscopy images focuses on accurately identifying the objects of interest in an image that can be used to investigate hypotheses about developmental or pathological processes and can be indicative of prognosis in patients. Detection is also considered to be the preliminary step for solving subsequent problems, such as segmentation and tracking for various biological applications. Segmentation of the desired structures and regions in microscopy images require pixel-level labels to uniquely identify the individual structures and regions with contours for morphological and physiological analysis. Distributions of features extracted from the segmented regions can be used to compare normal versus disease or normal versus wild-type populations. Segmentation can be considered as a precursor for solving classification, reconstruction, and tracking problems in microscopy images. In this chapter, we discuss how the field of microscopic image analysis has progressed over the years, starting with traditional approaches and then followed by the study of learning algorithms. Because there is a lot of variability in microscopy data, it is essential to study learning algorithms that can adapt to these changes. We focus on deep learning approaches with convolutional neural networks (CNNs), as well as hierarchical methods for segmentation and detection in optical and electron microscopy images. Limitation of training data is one of the significant problems; hence, we explore solutions to learn better models with minimal user annotations.



Leveraging 31 Million Google Street View Images to Characterize Built Environments and Examine County Health Outcomes
Q. C Nguyen, J. M. Keralis, P. Dwivedi, A. E. Ng, M. Javanmardi, S. Khanna, Y. Huang, K. D. Brunisholz, A. Kumar, T. Tasdizen. In Public Health Reports, Vol. 136, No. 2, SAGE Publications, pp. 201-211. 2021.
DOI: doi.org/10.1177/0033354920968799

Objectives
Built environments can affect health, but data in many geographic areas are limited. We used a big data source to create national indicators of neighborhood quality and assess their associations with health.

Methods
We leveraged computer vision and Google Street View images accessed from December 15, 2017, through July 17, 2018, to detect features of the built environment (presence of a crosswalk, non–single-family home, single-lane roads, and visible utility wires) for 2916 US counties. We used multivariate linear regression models to determine associations between features of the built environment and county-level health outcomes (prevalence of adult obesity, prevalence of diabetes, physical inactivity, frequent physical and mental distress, poor or fair self-rated health, and premature death [in years of potential life lost]).
Results
Compared with counties with the least number of crosswalks, counties with the most crosswalks were associated with decreases of 1.3%, 2.7%, and 1.3% of adult obesity, physical inactivity, and fair or poor self-rated health, respectively, and 477 fewer years of potential life lost before age 75 (per 100 000 population). The presence of non–single-family homes was associated with lower levels of all health outcomes except for premature death. The presence of single-lane roads was associated with an increase in physical inactivity, frequent physical distress, and fair or poor self-rated health. Visible utility wires were associated with increases in adult obesity, diabetes, physical and mental distress, and fair or poor self-rated health.
Conclusions
The use of computer vision and big data image sources makes possible national studies of the built environm