Sushil Mittal

Detection, grading and classification of coronary stenoses in computed tomography angiography

Recently conducted clinical studies prove the utility of Coronary Computed Tomography Angiography (CCTA) as a viable alternative to invasive angiography for the detection of Coronary Artery Disease (CAD). This has lead to the development of several algorithms for automatic detection and grading of coronary stenoses. However, most of these methods focus on detecting calcified plaques only. A few methods that can also detect and grade non-calcified plaques require substantial user involvement. In this paper, we propose a fast and fully automatic system that is capable of detecting, grading and classifying coronary stenoses in CCTA caused by all types of plaques. We propose a four-step approach including a learning-based centerline verification step and a lumen cross-section estimation step using random regression forests. We show state-of-the-art performance of our method in experiments conducted on a set of 229 CCTA volumes. With an average processing time of 1.8 seconds per case after centerline extraction, our method is significantly faster than competing approaches.

Semi-Supervised Kernel Mean Shift Clustering

Mean shift clustering is a powerful nonparametric technique that does not require prior knowledge of the number of clusters and does not constrain the shape of the clusters. However, being completely unsupervised, its performance suffers when the original distance metric fails to capture the underlying cluster structure. Despite recent advances in semi-supervised clustering methods, there has been little effort towards incorporating supervision into mean shift. We propose a semi-supervised framework for kernel mean shift clustering (SKMS) that uses only pairwise constraints to guide the clustering procedure. The points are first mapped to a high-dimensional kernel space where the constraints are imposed by a linear transformation of the mapped points. This is achieved by modifying the initial kernel matrix by minimizing a log det divergence-based objective function. We show the advantages of SKMS by evaluating its performance on various synthetic and real datasets while comparing with state-of-the-art semi-supervised clustering algorithms.

Generalized Projection-Based M-Estimator

We propose a novel robust estimation algorithm - the generalized projection-based M-estimator (gpbM), which does not require the user to specify any scale parameters. The algorithm is general and can handle heteroscedastic data with multiple linear constraints for single and multicarrier problems. The gpbM has three distinct stages - scale estimation, robust model estimation, and inlier/outlier dichotomy. In contrast, in its predecessor pbM, each model hypotheses was associated with a different scale estimate. For data containing multiple inlier structures with generally different noise covariances, the estimator iteratively determines one structure at a time. The model estimation can be further optimized by using Grassmann manifold theory. We present several homoscedastic and heteroscedastic synthetic and real-world computer vision problems with single and multiple carriers.

Sequential Decision Making Algorithms for Port of Entry Inspection: Overcoming Computational Challenges

Following work of Stroud and Saeger and Anand et al., we formulate a port of entry inspection sequencing task as a problem of finding an optimal binary decision tree for an appropriate Boolean decision function. We report on new algorithms that are more efficient computationally than those presented by Stroud and Saeger and Anand et al. We achieve these efficiencies through a combination of specific numerical methods for finding optimal thresholds for sensor functions and a novel binary decision tree search algorithm that operates on a space of potentially acceptable binary decision trees.

Conjugate gradient on Grassmann manifolds for robust subspace estimation

Most geometric computer vision problems involve orthogonality constraints. An important subclass of these problems is subspace estimation, which can be equivalently formulated into an optimization problem on Grassmann manifolds. In this paper, we propose to use the conjugate gradient algorithm on Grassmann manifolds for robust subspace estimation in conjunction with the recently introduced generalized projection based M-Estimator (gpbM). The gpbM method is an elemental subset-based robust estimation algorithm that can process heteroscedastic data without any user intervention. We show that by optimizing the orthogonal parameter matrix on Grassmann manifolds, the performance of the gpbM algorithm improves significantly. Results on synthetic and real data are presented.

Generalized projection based M-estimator: Theory and applications

We introduce a robust estimator called generalized projection based M-estimator (gpbM) which does not require the user to specify any scale parameters. For multiple inlier structures, with different noise covariances, the estimator iteratively determines one inlier structure at a time. Unlike pbM, where the scale of the inlier noise is estimated simultaneously with the model parameters, gpbM has three distinct stages–scale estimation, robust model estimation and inlier/outlier dichotomy. We evaluate our performance on challenging synthetic data, face image clustering upto ten different faces from Yale Face Database B and multi-body projective motion segmentation problem on Hopkins155 dataset. Results of state-of-the-art methods are presented for comparison.

Association between trauma center type and mortality among injured adolescent patients

IMPORTANCE Although data obtained from regional trauma systems demonstrate improved outcomes for children treated at pediatric trauma centers (PTCs) compared with those treated at adult trauma centers (ATCs), differences in mortality have not been consistently observed for adolescents. Because trauma is the leading cause of death and acquired disability among adolescents, it is important to better define differences in outcomes among injured adolescents by using national data. OBJECTIVES To use a national data set to compare mortality of injured adolescents treated at ATCs, PTCs, or mixed trauma centers (MTCs) that treat both pediatric and adult trauma patients and to determine the final discharge disposition of survivors at different center types. DESIGN, SETTING, AND PARTICIPANTS Data from level I and II trauma centers participating in the 2010 National Trauma Data Bank (January 1 to December 31, 2010) were used to create multilevel models accounting for center-specific effects to evaluate the association of center characteristics (PTC, ATC, or MTC) on mortality among patients aged 15 to 19 years who were treated for a blunt or penetrating injury. The models controlled for sex; mechanism of injury (blunt vs penetrating); injuries sustained, based on the Abbreviated Injury Scale scores (post-dot values <3 or ≥3 by body region); initial systolic blood pressure; and Glasgow Coma Scale scores. Missing data were managed using multiple imputation, accounting for multilevel data structure. Data analysis was conducted from January 15, 2013, to March 15, 2016. EXPOSURES Type of trauma center. MAIN OUTCOMES AND MEASURES Mortality at each center type. RESULTS Among 29 613 injured adolescents (mean [SD] age, 17.3 [1.4] years; 72.7% male), most were treated at ATCs (20 402 [68.9%]), with the remainder at MTCs (7572 [25.6%]) or PTCs (1639 [5.5%]). Adolescents treated at PTCs were more likely to be injured by a blunt than penetrating injury mechanism (91.4%) compared with those treated at ATCs (80.4%) or MTCs (84.6%). Mortality was higher among adolescents treated at ATCs and MTCs than those treated at PTCs (3.2% and 3.5% vs 0.4%; P < .001). The adjusted odds of mortality were higher at ATCs (odds ratio, 4.19; 95% CI, 1.30-13.51) and MTCs (odds ratio, 6.68; 95% CI, 2.03-21.99) compared with PTCs but was not different between level I and II centers (odds ratio, 0.76; 95% CI, 0.59-0.99). CONCLUSION AND RELEVANCE Mortality among injured adolescents was lower among those treated at PTCs, compared with those treated at ATCs and MTCs. Defining resource and patient features that account for these observed differences is needed to optimize adolescent outcomes after injury.

High-dimensional, massive sample-size Cox proportional hazards regression for survival analysis

Survival analysis endures as an old, yet active research field with applications that spread across many domains. Continuing improvements in data acquisition techniques pose constant challenges in applying existing survival analysis methods to these emerging data sets. In this paper, we present tools for fitting regularized Cox survival analysis models on high-dimensional, massive sample-size (HDMSS) data using a variant of the cyclic coordinate descent optimization technique tailored for the sparsity that HDMSS data often present. Experiments on two real data examples demonstrate that efficient analyses of HDMSS data using these tools result in improved predictive performance and calibration.

Large-scale parametric survival analysis.

Survival analysis has been a topic of active statistical research in the past few decades with applications spread across several areas. Traditional applications usually consider data with only a small numbers of predictors with a few hundreds or thousands of observations. Recent advances in data acquisition techniques and computation power have led to considerable interest in analyzing very-high-dimensional data where the number of predictor variables and the number of observations range between 10(4) and 10(6). In this paper, we present a tool for performing large-scale regularized parametric survival analysis using a variant of the cyclic coordinate descent method. Through our experiments on two real data sets, we show that application of regularized models to high-dimensional data avoids overfitting and can provide improved predictive performance and calibration over corresponding low-dimensional models.

Fast automatic detection of calcified coronary lesions in 3d cardiac CT images

Even with the recent advances in multidetector computed tomography (MDCT) imaging techniques, detection of calcified coronary lesions remains a highly tedious task. Noise, blooming and motion artifacts etc. add to its complication. We propose a novel learning-based, fully automatic algorithm for detection of calcified lesions in contrast-enhanced CT data. We compare and evaluate the performance of two supervised learning methods. Both these methods use rotation invariant features that are extracted along the centerline of the coronary. Our approach is quite robust to the estimates of the centerline and works well in practice. We are able to achieve average detection times of 0.67 and 0.82 seconds per volume using the two methods.