Browsing by Author "Cobzas, Dana"

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    496 Capstone: AR.t
    (2020) Driedger, Andre; Ansorger, Anneliese; Galay, Chance; Lafitte, Chanelle; Cobzas, Dana
    Original artwork is often very expensive; being able to see how a painting will look on a wall before you buy is advantageous. As a collaborative project between the MacEwan Computer Science and Design departments, we set out to do develop an AR app that can be used by by consumers to shop for art on the walls of their homes and offices. Existing mobile AR applications cannot identify vertical surfaces, such as walls. Our solution is to implement a target image that can be posted onto vertical surfaces to be detected by our app. We developed an OpenCV prototype to test this method of using object-detection to set a starting point for subsequent tracking. The prototype was successful in rendering 3d objects, true to scale, onto walls. Next, we developed an Android version utilizing Google's ARCore toolkit. This also delivered good results. Ultimately, we were successful in showcasing art on walls using smartphones in real-time.
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    A deep level set method for image segmentation
    (2017) Tang, Min; Valipour, Sepehr; Zhang, Zichen; Cobzas, Dana; Jagersand, Martin
    This paper proposes a novel image segmentation approach that integrates fully convolutional networks (FCNs) with a level set model. Compared with a FCN, the integrated method can incorporate smoothing and prior information to achieve an accurate segmentation. Furthermore, different than using the level set model as a post-processing tool, we integrate it into the training phase to fine-tune the FCN. This allows the use of unlabeled data during training in a semi-supervised setting. Using two types of medical imaging data (liver CT and left ventricle MRI data), we show that the integrated method achieves good performance even when little training data is available, outperforming the FCN or the level set model alone.
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    Android app demo
    (2020) Driedger, Andre; Ansorger, Anneliese; Galay, Chance; Lafitte, Chanelle; Cobzas, Dana
    For the app, we developed an Android version utilizing Google's ARCore toolkit. The Design students prototyped screens for user profiles, buying art, as well as filtering and browsing functionality. This functionality has not yet been implemented, and we instead chose to focus on the AR screens. The user can browse through and preview different paintings and frames.
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    AR.T deliver report
    (2020) Lafitte, Chanelle; Galay, Chance; Cobzas, Dana
    Original artwork is often very expensive; being able to see how a painting will look on a wall before you buy is advantageous. As a collaborative project between the MacEwan Computer Science and Design departments, we set out to do develop an AR app that can be used by by consumers to shop for art on the walls of their homes and offices. Existing mobile AR applications cannot identify vertical surfaces, such as walls. Our solution is to implement a target image that can be posted onto vertical surfaces to be detected by our app. We developed an OpenCV prototype to test this method of using object-detection to set a starting point for subsequent tracking. The prototype was successful in rendering 3d objects, true to scale, onto walls. Next, we developed an Android version utilizing Google's ARCore toolkit. This also delivered good results. Ultimately, we were successful in showcasing art on walls using smartphones in real-time.
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    Creative, interdisciplinary undergraduate research: an educational cell biology video game designed by students for students
    (2020) Sperano, Isabelle; Shaw, Ross; Andruchow, Robert; Cobzas, Dana; Efird, Cory; Brookwell, Brian; Deng, William
    In a three-year, practice-based, creative research project, the team designed a video game for undergraduate biology students that aimed to find the right balance between educational content and entertainment. The project involved 7 faculty members and 14 undergraduate students from biological science, design, computer science, and music. This nontraditional approach to research was attractive to students. Working on an interdisciplinary practice-based research project required strategies related to timeline, recruitment, funding, team management, and mentoring. Although this project was time-consuming and full of challenges, it created meaningful learning experiences not only for students but also for faculty members.
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    Developmental hip dysplasia diagnosis at three-dimensional US: a multicenter study
    (2018) Zonoobi, Dornoosh; Hareendranathan, Abhilash; Mostofi, Emanuel; Mabee, Myles; Pasha, Saba; Cobzas, Dana; Rao, Padma; Dulai, Sukhdeep K.; Kapur, Jeevesh; Jaremko, Jacob L.
    Purpose: To validate accuracy of diagnosis of developmental dysplasia of the hip (DDH) from geometric properties of acetabular shape extracted from three-dimensional (3D) ultrasonography (US).
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    Discriminative analysis of regional evolution of iron and myelin/calcium in deep gray matter of multiple sclerosis and healthy subjects
    (2018) Elkady, Ahmed M.; Cobzas, Dana; Sun, Hongfu; Blevins, Gregg; Wilman, Alan H.
    Background: Combined R2* and quantitative susceptibility (QS) has been previously used in cross‐sectional multiple sclerosis (MS) studies to distinguish deep gray matter (DGM) iron accumulation and demyelination. Purpose: We propose and apply discriminative analysis of regional evolution (DARE) to define specific changes in MS and healthy DGM. Study Type: Longitudinal (baseline and 2‐year follow‐up) retrospective study. Subjects: Twenty‐seven relapsing‐remitting MS (RRMS), 17 progressive MS (PMS), and corresponding age‐matched healthy subjects. Field Strength/Sequence: 4.7T 10‐echo gradient‐echo acquisition. Assessment: Automatically segmented caudate nucleus (CN), thalamus (TH), putamen (PU), globus pallidus, red nucleus (RN), substantia nigra, and dentate nucleus were retrospectively analyzed to quantify regional volumes, bulk mean R2*, and bulk mean QS. DARE utilized combined R2* and QS localized changes to compute spatial extent, mean intensity, and total changes of DGM iron and myelin/calcium over 2 years. Statistical Tests: We used mixed factorial analysis for bulk analysis, nonparametric tests for DARE (α = 0.05), and multiple regression analysis using backward elimination of DGM structures (α = 0.05, P = 0.1) to regress bulk and DARE measures with the follow‐up Multiple Sclerosis Severity Score (MSSS). False detection rate correction was applied to all tests. Results: Bulk analysis only detected significant (Q ≤ 0.05) interaction effects in RRMS CN QS (η = 0.45; Q = 0.004) and PU volume (η = 0.38; Q = 0.034). DARE demonstrated significant group differences in all RRMS structures, and in all PMS structures except the RN. The largest RRMS effect size was CN total R2* iron decrease (r = 0.74; Q = 0.00002), and TH mean QS myelin/calcium decrease for PMS (r = 0.70; Q = 0.002). DARE iron increase using total QS demonstrated the highest correlation with MSSS (r = 0.68; Q = 0.0005).Data Conclusion: DARE enabled discriminative assessment of specific DGM changes over 2 years, where iron and myelin/calcium changes were the primary drivers in RRMS and PMS compared to age‐matched controls, respectively. Specific DARE measures of MS DGM correlated with follow‐up MSSS, and may reflect complex disease pathology.
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    Discriminative analysis of regional evolution of iron and myelin/calcium in deep gray matter of multiple sclerosis and healthy subjects
    (2018) Elkady, Ahmed M.; Cobzas, Dana; Sun, Hongfu; Blevins, Gregg; Wilman, Alan H.
    Combined R2* and quantitative susceptibility (QS) has been previously used in cross‐sectional multiple sclerosis (MS) studies to distinguish deep gray matter (DGM) iron accumulation and demyelination. We propose and apply discriminative analysis of regional evolution (DARE) to define specific changes in MS and healthy DGM. Longitudinal (baseline and 2‐year follow‐up) retrospective study. Twenty‐seven relapsing‐remitting MS (RRMS), 17 progressive MS (PMS), and corresponding age‐matched healthy subjects . Field Strength/Sequence: 4.7T 10‐echo gradient‐echo acquisition. Automatically segmented caudate nucleus (CN), thalamus (TH), putamen (PU), globus pallidus, red nucleus (RN), substantia nigra, and dentate nucleus were retrospectively analyzed to quantify regional volumes, bulk mean R2*, and bulk mean QS. DARE utilized combined R2* and QS localized changes to compute spatial extent, mean intensity, and total changes of DGM iron and myelin/calcium over 2 years. We used mixed factorial analysis for bulk analysis, nonparametric tests for DARE (α = 0.05), and multiple regression analysis using backward elimination of DGM structures (α = 0.05, P = 0.1) to regress bulk and DARE measures with the follow‐up Multiple Sclerosis Severity Score (MSSS). False detection rate correction was applied to all tests. Bulk analysis only detected significant (Q ≤ 0.05) interaction effects in RRMS CN QS (η = 0.45; Q = 0.004) and PU volume (η = 0.38; Q = 0.034). DARE demonstrated significant group differences in all RRMS structures, and in all PMS structures except the RN. The largest RRMS effect size was CN total R2* iron decrease (r = 0.74; Q = 0.00002), and TH mean QS myelin/calcium decrease for PMS (r = 0.70; Q = 0.002). DARE iron increase using total QS demonstrated the highest correlation with MSSS (r = 0.68; Q = 0.0005). DARE enabled discriminative assessment of specific DGM changes over 2 years, where iron and myelin/calcium changes were the primary drivers in RRMS and PMS compared to age‐matched controls, respectively. Specific DARE measures of MS DGM correlated with follow‐up MSSS, and may reflect complex disease pathology.
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    End-to-end detection-segmentation network with ROI convolution
    (2018) Zhang, Zichen; Tang, Min; Cobzas, Dana; Zonoobi, Dornoosh; Jagersand, Martin; Jaremko, Jacob L.
    We propose an end-to-end neural network that improves the segmentation accuracy of fully convolutional networks by incorporating a localization unit. This network performs object localization first, which is then used as a cue to guide the training of the segmentation network. We test the proposed method on a segmentation task of small objects on a clinical dataset of ultrasound images. We show that by jointly learning for detection and segmentation, the proposed network is able to improve the segmentation accuracy compared to only learning for segmentation.
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    Evaluation of automated computed tomography segmentation to assess body composition and mortality associations in cancer patients
    (2020) Cespedes Feliciano, Elizabeth M.; Popuri, Karteek; Cobzas, Dana; Baracos, Vickie E.; Beg, Mirza Faisal; Khan, Arafat Dad; Ma, Cydney; Chow, Vincent; Chow, Vincent; Prado, Carla M.; Xiao, Jingjie; Liu, Vincent; Chen, Wendy Y.; Meyerhardt, Jeffrey; Albers, Kathleen B.; Caan, Bette J.
    Background Body composition from computed tomography (CT) scans is associated with cancer outcomes including surgical complications, chemotoxicity, and survival. Most studies manually segment CT scans, but Automatic Body composition Analyser using Computed tomography image Segmentation (ABACS) software automatically segments muscle and adipose tissues to speed analysis. Here, we externally evaluate ABACS in an independent dataset. Methods Among patients with non‐metastatic colorectal (n = 3102) and breast (n = 2888) cancer diagnosed from 2005 to 2013 at Kaiser Permanente, expert raters annotated tissue areas at the third lumbar vertebra (L3). To compare ABACS segmentation results to manual analysis, we quantified the proportion of pixel‐level image overlap using Jaccard scores and agreement between methods using intra‐class correlation coefficients for continuous tissue areas. We examined performance overall and among subgroups defined by patient and imaging characteristics. To compare the strength of the mortality associations obtained from ABACS's segmentations to manual analysis, we computed Cox proportional hazards ratios (HRs) and 95% confidence intervals (95% CI) by tertile of tissue area. Results Mean ± SD age was 63 ± 11 years for colorectal cancer patients and 56 ± 12 for breast cancer patients. There was strong agreement between manual and automatic segmentations overall and within subgroups of age, sex, body mass index, and cancer stage: average Jaccard scores and intra‐class correlation coefficients exceeded 90% for all tissues. ABACS underestimated muscle and visceral and subcutaneous adipose tissue areas by 1–2% versus manual analysis: mean differences were small at −2.35, −1.97 and −2.38 cm2, respectively. ABACS's performance was lowest for the <2% of patients who were underweight or had anatomic abnormalities. ABACS and manual analysis produced similar associations with mortality; comparing the lowest to highest tertile of skeletal muscle from ABACS versus manual analysis, the HRs were 1.23 (95% CI: 1.00–1.52) versus 1.38 (95% CI: 1.11–1.70) for colorectal cancer patients and 1.30 (95% CI: 1.01–1.66) versus 1.29 (95% CI: 1.00–1.65) for breast cancer patients. Conclusions In the first study to externally evaluate a commercially available software to assess body composition, automated segmentation of muscle and adipose tissues using ABACS was similar to manual analysis and associated with mortality after non‐metastatic cancer. Automated methods will accelerate body composition research and, eventually, facilitate integration of body composition measures into clinical care.
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    Five year iron changes in relapsing-remitting multiple sclerosis deep gray matter compared to healthy controls
    (2019) Elkady, Ahmed M.; Cobzas, Dana; Sun, Hongfu; Seres, Peter; Blevins, Gregg; Wilman, Alan H.
    Relapsing-Remitting MS (RRMS) Deep Grey Matter (DGM) 5 year changes were examined using MRI measures of volume, transverse relaxation rate (R2*) and quantitative magnetic susceptibility (QS). By applying Discriminative Analysis of Regional Evolution (DARE), R2* and QS changes from iron and non-iron sources were separated. 25 RRMS and 25 age-matched control subjects were studied at baseline and 5-year follow-up. Bulk DGM mean R2* and QS of the caudate nucleus, putamen, thalamus and globus pallidus were analyzed using mixed factorial analysis (α = 0.05) with sex as a covariate, while DARE employed non-parametric analysis to study regional changes. Regression/correlation analysis was performed with disease duration and MS Severity Score (MSSS). No significant change in Extended Disability Status Score was found over 5 years (baseline = 2.4 ± 1.2; follow-up = 2.8 ± 1.3). Significant time effects were found for R2* in the caudate (Q = 0.000008; η2 = 0.36), putamen (Q = 0.0000007; η2 = 0.43), and globus pallidus (Q = 0.0000007; η2 = 0.43), while significant longitudinal effects were only found for QS in the putamen (Q = 0.002; η2 = 0.22). Significant bulk interaction was only found for thalamus volume (Q = 0.02; η2 = 0.20). Iron decrease was the only detected significant effect using DARE, and the highest significant DARE effect size was mean thalamus R2* iron decrease (Q = 0.002; η2 = 0.26). No significant correlations or regressions were demonstrated with clinical measures. Thalamic atrophy was the only bulk effect that demonstrated different rates of changes over 5 years compared to age-matched controls. DARE Iron decrease in regions of the caudate, putamen, and thalamus were prominent features in stable RRMS over 5 years.
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    Hippocampus segmentation on high resolution dffusion MRI
    (2021) Efird, Cory; Neumann, Samuel; Solar, Kevin G.; Beaulieu, Christian; Cobzas, Dana
    We introduce the first hippocampus segmentation method for a novel high resolution (1×1×1mm3) diffusion tensor imaging (DTI) protocol acquired in 5.5 minutes at 3T. A new augmentation technique uses subsets of the DTI dataset to create mean diffusion weighted images (DWI) with plausible noise and contrast variations. The augmented DWI along with fractional anisotropy (FA) and mean diffusivity (MD) maps are used as inputs to a powerful convolutional neural network architecture. The method is evaluated for robustness using a second diffusion protocol.
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    Low-rank plus sparse decomposition of fMRI data with application to Alzheimer's disease
    (2022) Tu, Wei; Fu, Fangfang; Kong, Linglong; Jiang, Bei; Cobzas, Dana; Huang, Chao
    Studying functional brain connectivity plays an important role in understanding how human brain functions and neuropsychological diseases such as autism, attention-deficit hyperactivity disorder, and Alzheimer's disease (AD). Functional magnetic resonance imaging (fMRI) is one of the most popularly used tool to construct functional brain connectivity. However, the presence of noises and outliers in fMRI blood oxygen level dependent (BOLD) signals might lead to unreliable and unstable results in the construction of connectivity matrix. In this paper, we propose a pipeline that enables us to estimate robust and stable connectivity matrix, which increases the detectability of group differences. In particular, a low-rank plus sparse (L + S) matrix decomposition technique is adopted to decompose the original signals, where the low-rank matrix L recovers the essential common features from regions of interest, and the sparse matrix S catches the sparse individual variability and potential outliers. On the basis of decomposed signals, we construct connectivity matrix using the proposed novel concentration inequality-based sparse estimator. In order to facilitate the comparisons, we also consider correlation, partial correlation, and graphical Lasso-based methods. Hypothesis testing is then conducted to detect group differences. The proposed pipeline is applied to rs-fMRI data in Alzheimer's disease neuroimaging initiative to detect AD-related biomarkers, and we show that the proposed pipeline provides accurate yet more stable results than using the original BOLD signals.
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    OpenCV laptop demo
    (2020) Driedger, Andre; Ansorger, Anneliese; Galay, Chance; Lafitte, Chanelle; Cobzas, Dana
    When we started the project, we had decided to make a program that would use feature matching to recognize a specific image (eg. a poster or sticker), find it’s orientation, and then display some kind of useful AR artifacts in the 3D space of our recognized image. We have implemented this in OpenCV, to show that we have an in-depth understanding of how AR works.
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    Progressive iron accumulation across multiple sclerosis phenotypes revealed by sparse classification of deep gray matter
    (2017) Elkady, Ahmed M.; Cobzas, Dana; Sun, Hongfu; Blevins, Gregg; Wilman, Alan H.
    To create an automated framework for localized analysis of deep gray matter (DGM) iron accumulation and demyelination using sparse classification by combining quantitative susceptibility (QS) and transverse relaxation rate (R2*) maps, for evaluation of DGM in multiple sclerosis (MS) phenotypes relative to healthy controls.R2*/QS maps were computed using a 4.7T 10‐echo gradient echo acquisition from 16 clinically isolated syndrome (CIS), 41 relapsing‐remitting (RR), 40 secondary‐progressive (SP), 13 primary‐progressive (PP) MS patients, and 75 controls. Sparse classification for R2*/QS maps of segmented caudate nucleus (CN), putamen (PU), thalamus (TH), and globus pallidus (GP) structures produced localized maps of iron/myelin in MS patients relative to controls. Paired t‐tests, with age as a covariate, were used to test for statistical significance (P ≤ 0.05).In addition to DGM structures found significantly different in patients compared to controls using whole region analysis, singular sparse analysis found significant results in RRMS PU R2* (P = 0.03), TH R2* (P = 0.04), CN QS (P = 0.04); in SPMS CN R2* (P = 0.04), GP R2* (P = 0.05); and in PPMS CN R2* (P = 0.04), TH QS (P = 0.04). All sparse regions were found to conform to an iron accumulation pattern of changes in R2*/QS, while none conformed to demyelination. Intersection of sparse R2*/QS regions also resulted in RRMS CN R2* becoming significant, while RRMS R2* TH and PPMS QS TH becoming insignificant. Common iron‐associated volumes in MS patients and their effect size progressively increased with advanced phenotypes.A localized technique for identifying sparse regions indicative of iron or myelin in the DGM was developed. Progressive iron accumulation with advanced MS phenotypes was demonstrated, as indicated by iron‐associated sparsity and effect size.
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    Progressive iron accumulation across multiple sclerosis phenotypes revealed by sparse classification of deep gray matter
    (2017) Elkady, Ahmed M.; Cobzas, Dana; Sun, Hongfu; Blevins, Gregg; Wilman, Alan H.
    Purpose: To create an automated framework for localized analysis of deep gray matter (DGM) iron accumulation and demyelination using sparse classification by combining quantitative susceptibility (QS) and transverse relaxation rate (R2*) maps, for evaluation of DGM in multiple sclerosis (MS) phenotypes relative to healthy controls. Materials and Methods: R2*/QS maps were computed using a 4.7T 10-echo gradient echo acquisition from 16 clinically isolated syndrome (CIS), 41 relapsing-remitting (RR), 40 secondary-progressive (SP), 13 primary-progressive (PP) MS patients, and 75 controls. Sparse classification for R2*/QS maps of segmented caudate nucleus (CN), putamen (PU), thalamus (TH), and globus pallidus (GP) structures produced localized maps of iron/myelin in MS patients relative to controls. Paired t-tests, with age as a covariate, were used to test for statistical significance (P ≤ 0.05).Results: In addition to DGM structures found significantly different in patients compared to controls using whole region analysis, singular sparse analysis found significant results in RRMS PU R2* (P = 0.03), TH R2* (P = 0.04), CN QS (P = 0.04); in SPMS CN R2* (P = 0.04), GP R2* (P = 0.05); and in PPMS CN R2* (P = 0.04), TH QS (P = 0.04). All sparse regions were found to conform to an iron accumulation pattern of changes in R2*/QS, while none conformed to demyelination. Intersection of sparse R2*/QS regions also resulted in RRMS CN R2* becoming significant, while RRMS R2* TH and PPMS QS TH becoming insignificant. Common iron-associated volumes in MS patients and their effect size progressively increased with advanced phenotypes. Conclusion: A localized technique for identifying sparse regions indicative of iron or myelin in the DGM was developed. Progressive iron accumulation with advanced MS phenotypes was demonstrated, as indicated by iron-associated sparsity and effect size.
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    Segmentation-by-detection: a cascade network for volumetric medical image segmentation
    (2018) Tang, Min; Zhang, Zichen; Cobzas, Dana; Jagersand, Martin; Jaremko, Jacob L.
    We propose an attention mechanism for 3D medical image segmentation. The method, named segmentation-by-detection, is a cascade of a detection module followed by a segmentation module. The detection module enables a region of interest to come to attention and produces a set of object region candidates which are further used as an attention model. Rather than dealing with the entire volume, the segmentation module distills the information from the potential region. This scheme is an efficient solution for volumetric data as it reduces the influence of the surrounding noise. This is especially important for medical data with low signal-to-noise ratio. Experimental results on 3D ultrasound data of the femoral head shows superiority of the proposed method when compared with a standard fully convolutional network like the U-Net.
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    Significant anatomy detection through sparse classification: a comparative study
    (2018) Zhang, Li; Cobzas, Dana; Wilman, Alan H.; Kong, Linglong
    We present a comparative study for discriminative anatomy detection in high dimensional neuroimaging data. While most studies solve this problem using mass univariate approaches, recent works show better accuracy and variable selection using a sparse classification model. Two types of image-based regularization methods have been proposed in the literature based on either a Graph Net (GN) model or a total variation (TV) model. These studies showed increased classification accuracy and interpretability of results when using image-based regularization, but did not look at the accuracy and quality of the recovered significant regions. In this paper, we theoretically prove bounds on the recovered sparse coefficients and the corresponding selected image regions in four models (two based on GN penalty and two based on TV penalty). Practically, we confirm the theoretical findings by measuring the accuracy of selected regions compared with ground truth on simulated data. We also evaluate the stability of recovered regions over cross-validation folds using real MRI data. Our findings show that the TV penalty is superior to the GN model. In addition, we showed that adding an l2 penalty improves the accuracy of estimated coefficients and selected significant regions for the both types of models.
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    Stable anatomy detection in multimodal imaging through sparse group regularization: a comparative study of iron accumulation in the aging brain
    (2021) Pietrosanu, Matthew; Zhang, Li; Seres, Peter; Elkady, Ahmed M.; Wilman, Alan H.; Kong, Linglong; Cobzas, Dana
    Multimodal neuroimaging provides a rich source of data for identifying brain regions associated with disease progression and aging. However, present studies still typically analyze modalities separately or aggregate voxel-wise measurements and analyses to the structural level, thus reducing statistical power. As a central example, previous works have used two quantitative MRI parameters—R2* and quantitative susceptibility (QS)—to study changes in iron associated with aging in healthy and multiple sclerosis subjects, but failed to simultaneously account for both. In this article, we propose a unified framework that combines information from multiple imaging modalities and regularizes estimates for increased interpretability, generalizability, and stability. Our work focuses on joint region detection problems where overlap between effect supports across modalities is encouraged but not strictly enforced. To achieve this, we combine L1 (lasso), total variation (TV), and L2 group lasso penalties. While the TV penalty encourages geometric regularization by controlling estimate variability and support boundary geometry, the group lasso penalty accounts for similarities in the support between imaging modalities. We address the computational difficulty in this regularization scheme with an alternating direction method of multipliers (ADMM) optimizer. In a neuroimaging application, we compare our method against independent sparse and joint sparse models using a dataset of R2* and QS maps derived from MRI scans of 113 healthy controls: our method produces clinically-interpretable regions where specific iron changes are associated with healthy aging. Together with results across multiple simulation studies, we conclude that our approach identifies regions that are more strongly associated with the variable of interest (e.g., age), more accurate, and more stable with respect to training data variability. This work makes progress toward a stable and interpretable multimodal imaging analysis framework for studying disease-related changes in brain structure and can be extended for classification and disease prediction tasks.
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    User testing for serious game design: improving the player experience
    (2022) Shaw, Ross; Sperano, Isabelle; Andruchow, Robert; Cobzas, Dana
    This case study reflects on our use of user testing during a research project in which we designed a serious video game, “Life on the Edge.” The target audience of the game is first-year post-secondary biology students. As we designed the game, user testing was a critical component that allowed us to identify issues. Any issues that interfere with the flow or enjoyment of a video game can be distracting to players. In what follows, we will describe the research design and discuss the processes for testing a serious video game that will allow you to identify game issues successfully. How you recruit participants, test players, and prioritize player feedback is a component of effective user testing and improving your game. With user testing, we were able to identify problems in the game, prioritize them, and address them. By using variable user testing methods, you can adapt to the changing needs of your game project and develop a successful serious video game.