Nirmalya Ghosh

Automated ischemic lesion detection in a neonatal model of hypoxic ischemic injury.

To develop and compare an automated detection system for ischemic lesions in a neonatal model of bilateral carotid artery occlusion with hypoxia (BCAO‐H) from T2 weighted MRI (T2WI) to the currently used “gold standard” of manual segmentation.

Reparative effects of neural stem cells in neonatal rats with hypoxic–ischemic injury are not influenced by host sex

Background:Gender is increasingly recognized as an important influence on brain development, disease susceptibility, and response to pharmacologic/rehabilitative treatments. In regenerative medicine, it remains entirely unknown whether there is an interaction between transplanted stem cells and host gender that might bias efficacy and safety in some patients but not others.Methods:We examined the role of recipient gender in a neonatal rat hypoxic–ischemic injury (HII) model, treated with female human neuronal stem cells (hNSCs), labeled with superparamagnetic iron oxide particles implanted into the contralateral cerebral ventricle. We monitored HII evolution (by magnetic resonance imaging, histopathology, behavioral testing) and hNSC fate (migration, replication, viability).Results:Recipient gender after implantation did not influence the volume or location of ischemic injury (1, 30, or 90 d) or behavior (90 d). Superparamagnetic iron oxide labeling did not influence HII evolution. Implantation had its greatest benefit on mild/moderate injuries, which remained stable rather than increasing as in severe HII as is the natural history for such lesions.Conclusion:Our results suggest that hNSC treatment (including using hNSCs that are prelabeled with iron to allow tracking in real time by magnetic resonance imaging) would be equally safe and effective for male and female human newborns with mild-to-moderate HII.

Brain irradiation improves focal cerebral ischemia recovery in aged rats

BACKGROUND Studies have shown that aging is a significant factor in worsening stroke outcomes. While many mechanisms may aggravate brain injury in the elderly, one such potential system may involve increased glial proliferation in the aged stroke patient that could result in increased scar formation. We hypothesized that in aged rats a single brain-only exposure to a low radiation dose prior to focal brain ischemia would reduce glial proliferation and confer a long-term neuroprotective effect. METHODS Brain-only proton irradiation (8 Gy) was performed ten days prior to middle cerebral artery occlusion (MCAO) in aged male rats. Magnetic resonance imaging (MRI) was undertaken in naive, radiation-only (Rad), MCAO, and MCAO+Rad groups at 2, 14 and 28 days post-stroke followed by immunohistochemistry (day 28). RESULTS Ischemic lesion volume in MCAO+Rad group was decreased by 50.7% with an accelerated temporal reduction in peri-lesional brain edema and increased water mobility within the ischemic core (39.8%) compared to MCAO-only rats. In the peri-lesional brain region of MCAO+Rad rats there was a decreased scar formation (49%, glial fibrillary acidic protein), brain tissue sclerosis (30%, aquaporin-4) and necrosis/apoptosis (58%, TUNEL positive cells) compared to those in MCAO animals. CONCLUSION In aged animals a single exposure to brain-only radiation prior to focal cerebral ischemia is neuroprotective as it prevents glial hyperproliferation, progressive brain tissue sclerosis and reduces the apoptosis/necrosis in the peri-lesional region. Decreased lesion volume is in agreement with accelerated reduction of brain edema in these animals.

Incremental Unsupervised Three-Dimensional Vehicle Model Learning From Video

In this paper, we present a new generic model-based approach for building 3-D models of vehicles from color video from a single uncalibrated traffic-surveillance camera. We propose a novel directional template method that uses trigonometric relations of the 2-D features and geometric relations of a single 3-D generic vehicle model to map 2-D features to 3-D in the face of projection and foreshortening effects. We use novel hierarchical structural similarity measures to evaluate these single-frame-based 3-D estimates with respect to the generic vehicle model. Using these similarities, we adopt a weighted clustering technique to build a 3-D model of the vehicle for the current frame. The 3-D features are then adaptively clustered again over the frame sequence to generate an incremental 3-D model of the vehicle. Results are shown for several simulated and real traffic videos in an uncontrolled setup. Finally, the results are evaluated by the same structural performance measure, underscoring the usefulness of incremental learning. The performance of the proposed method for several types of vehicles in two considerably different traffic spots is very promising to encourage its applicability in 3-D reconstruction of other rigid objects in video.

Incremental Vehicle 3-D Modeling from Video

In this paper, we present a new model-based approach for building 3D models of vehicles from color video provided by a traffic surveillance camera. We incrementally build 3D models using a clustering technique. Geometrical relations based on 3D generic vehicle model map 2D features to 3D. The 3D features are then adaptively clustered over the frame sequence to incrementally generate the 3D model of the vehicle. Results are shown for both simulated and real traffic video. They are evaluated by a new structural performance measure underscoring usefulness of incremental learning

The late phase of post-stroke neurorepair in aged rats is reflected by MRI-based measures.

Non-invasive criteria determining the progress of brain healing are especially important in aging, providing a case-specific therapeutic strategy in populations with dysregulated neurorepair mechanisms. We hypothesized that temporal evolution of magnetic resonance imaging (MRI) of T2 tissue relaxation values correlate with neurological severity scores (NS), and provide a robust indicator of healing in the aging brain after stroke. Pre-treatment of aged rats with brain-only proton irradiation was undertaken to pre-condition the inflammatory system. Irradiation was performed 10days prior to right middle cerebral artery occlusion (MCAO) for 50min (MCAO+Rad). Control rats included naïve (no ischemia, no radiation), irradiated-only (Rad), irradiated ischemic, or ischemic-only (MCAO). MRI and NS were obtained at 3, 14 and 28days post-stroke. At 28days post-stroke, immunofluorescence for visualizing blood vessels (Von Willebrand factor; vWF), neurons (neuronal nuclear antigen; NeuN), astrocytes (glial fibrillary acidic protein; GFAP), activated microglia/macrophages (ionized calcium-binding adapter molecule 1, Iba1), T-lymphocytes (CD3), phagocytes (ED1) and apoptotic cells (caspase-3) was assessed. We found a positive T2-NS correlation in irradiated, ischemic rats that corresponded to late-stage brain recovery. Late-stage brain recovery was characterized by improved neovascularization, formation of glio-vascular complexes (visualized by GFAP/vWF) and enhanced neuronal viability (by NeuN/caspase-3) in the peri-lesional zone. The immune response plateaued at the late stage of repair as evidenced by significantly decreased expression (41.7%) and distribution of phagocytes (phagocytic rim decreased 44.6%). We also found reduced infiltration of T-lymphocytes (CD3) in the brain and normalization of blood lymphocytes. The observed T2-NS correlations may provide a simple MRI-based criterion for recognition of regenerative brain transformation in aged patients following stroke. Selective activation of innate immunity and accelerated transition from pro-inflammatory to pro-healing macrophage phenotypes induced by localized brain irradiation is a potential mechanism for enhancing repair ability in the elderly.

Computational Analysis: A Bridge to Translational Stroke Treatment

Objective rapid quantification of injury using computational methods can improve the assessment of the degree of stroke injury, aid in the selection of patients for early or specific treatments, and monitor the evolution of injury and recovery. In this chapter, we use neonatal ischemia as a case-study of the application of several computational methods that in fact are generic and applicable across the age and disease spectrum. We provide a summary of current computational approaches used for injury detection, including Gaussian mixture models (GMM), Markov random fields (MRFs), normalized graph cut, and K-means clustering. We also describe more recent automated approaches to segment the region(s) of ischemic injury including hierarchical region splitting, support vector machine, a brain symmetry/asymmetry integrated model, and a watershed method that are robust at different developmental stages. We conclude with our assessment of probable future research directions in the field of computational noninvasive stroke analysis such as automated detection of the ischemic core and penumbra, monitoring of implanted neuronal stem cells in the ischemic brain, injury localization specific to different brain anatomical regions, and quantification of stroke evolution, recovery and spatiotemporal interactions between injury volume/severity and treatment. Computational analysis is expected to open a new horizon in current clinical and translational stroke research by exploratory data mining that is not detectable using the standard “methods” of visual assessment of imaging data.

How current BNs fail to represent evolvable pattern recognition problems and a proposed solution

In the real world, systems/processes often evolve without fixed and predictable dynamic models. To represent such applications we need uncertainty models, like Bayesian nets (BN) that are formed online and in a self-evolving data-driven way. But current BN frameworks cannot handle simultaneous scalability in the model structure and causal relations. We show how current BNs fail in different applications from several fields, ranging from computer vision to database retrieval to medical diagnostics. We propose a novel structure modifiable adaptive reason-building temporal Bayesian networks (SmartBN) that has scalability for uncertainty in both, structures and causal relations. We evaluate its performance for a 3D model building application for vehicles in traffic video.

Evolving Bayesian Graph for Three-Dimensional Vehicle Model Building From Video

Traffic videos often capture slowly changing views of moving vehicles. These different and incrementally related views provide visual cues for 3-D perception of the vehicles from 2-D videos. This paper focuses on 3-D model building of multiple vehicles with different shapes from a single generic 3-D vehicle model by incrementally accumulating evidences in streaming traffic videos collected from a single static uncalibrated camera. When we do not know a priori the class of the following vehicle to be seen (which is true in a real traffic scenario), a flexible and evolvable Bayesian graphical model (BGM) is required, where the number of nodes, the structure of links between them, and the associated conditional probability distributions can change on the fly. Current BGMs fail to provide such online flexibility. We propose a novel BGM, which is called structure-modifiable adaptive reason-building temporal Bayesian graph (SmartBG), that self-modifies in a data-driven way to model uncertainty propagation in 3-D vehicle model building from 2-D video features, where only a subset of the 2-D vehicle features is visible at any time point, e.g., out of field-of-view (entry/exit) and self-occlusion. Uncertainties are used as relative weights to fuse evidences and to compute the overall reliability of the generated models. Results for different vehicles from several traffic videos and two different viewpoints demonstrate the performance of the proposed method.

Bayesian based 3D shape reconstruction from video

In a video sequence with a 3D rigid object moving, changing shapes of the 2D projections provide interrelated spatio-temporal cues for incremental 3D shape reconstruction. This paper describes a probabilistic approach for intelligent view-integration to build 3D model of vehicles from traffic videos collected from an uncalibrated static camera. The proposed Bayesian net framework allows the handling of uncertainties in a systematic manner. The performance is verified with several types of vehicles in different videos.