Infection diagnosis in hydrocephalus CT images: a domain enriched attention learning approach.

Objective. Hydrocephalus is the leading indication for pediatric neurosurgical care worldwide. Identification of postinfectious hydrocephalus (PIH) verses non-postinfectious hydrocephalus, as well as the pathogen involved in PIH is crucial for developing an appropriate treatment plan. Accurate identification requires clinical diagnosis by neuroscientists and microbiological analysis, which are time-consuming and expensive. In this study, we develop a domain enriched AI method for computerized tomography (CT)-based infection diagnosis in hydrocephalic imagery. State-of-the-art (SOTA) convolutional neural network (CNN) approaches form an attractive neural engineering solution for addressing this problem as pathogen-specific features need discovery. Yet black-box deep networks often need unrealistic abundant training data and are not easily interpreted.Approach. In this paper, a novel brain attention regularizer is proposed, which encourages the CNN to put more focus inside brain regions in its feature extraction and decision making. Our approach is then extended to a hybrid 2D/3D network that mines inter-slice information. A new strategy of regularization is also designed for enabling collaboration between 2D and 3D branches.Main results. Our proposed method achieves SOTA results on a CURE Children's Hospital of Uganda dataset with an accuracy of 95.8% in hydrocephalus classification and 84% in pathogen classification. Statistical analysis is performed to demonstrate that our proposed methods obtain significant improvements over the existing SOTA alternatives.Significance. Such attention regularized learning has particularly pronounced benefits in regimes where training data may be limited, thereby enhancing generalizability. To the best of our knowledge, our findings are unique among early efforts in interpretable AI-based models for classification of hydrocephalus and underlying pathogen using CT scans.

Investigating Useful Features for Overall Survival Prediction in Patients with Low-Grade Glioma Using Histology Slides.

Glioma, characterized by neoplastic growth in the brain, is a life-threatening condition that, in most cases, ultimately leads to death. Typical analysis of glioma development involves observation of brain tissue in the form of a histology slide under a microscope. Although brain histology images have much potential for predicting patient outcomes such as overall survival (OS), they are rarely used as the sole predictors due challenges presented by unique characteristics of brain tissue histology. However, utilizing histology in predicting overall survival can be useful for treatment and quality-of-life for patients with early-stage glioma. In this study, we investigate the use of deep learning models on histology slides combined with simple descriptor data (age and glioma subtype) as a predictor of (OS) in patients with low-grade glioma (LGG). Using novel clinical data, we show that models which are more attentive to discriminative features of the image will confer better predictions than generic models (82.7 and 65.3 AUC RFD-Net and Baseline VGG16 model, respectively). Additionally, we show that adding age and subtype information to a histology image-based model may provide greater robustness in the model than using the image alone (3.8 and 4.3 stds for RFD-Net and Baseline VGG16 model with 3-fold CV, respectively), while a model based on image and age but not subtype may confer the best predictive results (83.7 and 82.0 AUC for RFD-Net + age and RFD-Net + age + subtype, respectively). Clinical relevance- This study establishes important criteria for deep learning models which predict OS using histology and basic clinical data from LGG patients.

Robust Deep 3D Blood Vessel Segmentation Using Structural Priors.

Deep learning has enabled significant improvements in the accuracy of 3D blood vessel segmentation. Open challenges remain in scenarios where labeled 3D segmentation maps for training are severely limited, as is often the case in practice, and in ensuring robustness to noise. Inspired by the observation that 3D vessel structures project onto 2D image slices with informative and unique edge profiles, we propose a novel deep 3D vessel segmentation network guided by edge profiles. Our network architecture comprises a shared encoder and two decoders that learn segmentation maps and edge profiles jointly. 3D context is mined in both the segmentation and edge prediction branches by employing bidirectional convolutional long-short term memory (BCLSTM) modules. 3D features from the two branches are concatenated to facilitate learning of the segmentation map. As a key contribution, we introduce new regularization terms that: a) capture the local homogeneity of 3D blood vessel volumes in the presence of biomarkers; and b) ensure performance robustness to domain-specific noise by suppressing false positive responses. Experiments on benchmark datasets with ground truth labels reveal that the proposed approach outperforms state-of-the-art techniques on standard measures such as DICE overlap and mean Intersection-over-Union. The performance gains of our method are even more pronounced when training is limited. Furthermore, the computational cost of our network inference is among the lowest compared with state-of-the-art.

Preoperative risk and postoperative outcome from subdural fluid collections in African infants with postinfectious hydrocephalus.

OBJECTIVE: This study investigated the incidence of postoperative subdural collections in a cohort of African infants with postinfectious hydrocephalus. The authors sought to identify preoperative factors associated with increased risk of development of subdural collections and to characterize associations between subdural collections and postoperative outcomes. METHODS: The study was a post hoc analysis of a randomized controlled trial at a single center in Mbale, Uganda, involving infants (age < 180 days) with postinfectious hydrocephalus randomized to receive either an endoscopic third ventriculostomy plus choroid plexus cauterization or a ventriculoperitoneal shunt. Patients underwent assessment with the Bayley Scales of Infant and Toddler Development, Third Edition (Bayley-III; sometimes referred to as BSID-III) and CT scans preoperatively and then at 6, 12, and 24 months postoperatively. Volumes of brain, CSF, and subdural fluid were calculated, and z-scores from the median were determined from normative curves for CSF accumulation and brain growth. Linear and logistic regression models were used to characterize the association between preoperative CSF volume and the postoperative presence and size of subdural collection 6 and 12 months after surgery. Linear regression and smoothing spline ANOVA were used to describe the relationship between subdural fluid volume and cognitive scores. Causal mediation analysis distinguished between the direct and indirect effects of the presence of a subdural collection on cognitive scores. RESULTS: Subdural collections were more common in shunt-treated patients and those with larger preoperative CSF volumes. Subdural fluid volumes were linearly related to preoperative CSF volumes. In terms of outcomes, the Bayley-III cognitive score was linearly related to subdural fluid volume. The distribution of cognitive scores was significantly different for patients with and those without subdural collections from 11 to 24 months of age. The presence of a subdural collection was associated with lower cognitive scores and smaller brain volume 12 months after surgery. Causal mediation analysis demonstrated evidence supporting both a direct (76%) and indirect (24%) effect (through brain volume) of subdural collections on cognitive scores. CONCLUSIONS: Larger preoperative CSF volume and shunt surgery were found to be risk factors for postoperative subdural collection. The size and presence of a subdural collection were negatively associated with cognitive outcomes and brain volume 12 months after surgery. These results have suggested that preoperative CSF volumes could be used for risk stratification for treatment decision-making and that future clinical trials of alternative shunt technologies to reduce overdrainage should be considered.

Assessing the utility of low resolution brain imaging: treatment of infant hydrocephalus.

As low-field MRI technology is being disseminated into clinical settings around the world, it is important to assess the image quality required to properly diagnose and treat a given disease and evaluate the role of machine learning algorithms, such as deep learning, in the enhancement of lower quality images. In this post hoc analysis of an ongoing randomized clinical trial, we assessed the diagnostic utility of reduced-quality and deep learning enhanced images for hydrocephalus treatment planning. CT images of post-infectious infant hydrocephalus were degraded in terms of spatial resolution, noise, and contrast between brain and CSF and enhanced using deep learning algorithms. Both degraded and enhanced images were presented to three experienced pediatric neurosurgeons accustomed to working in low- to middle-income countries (LMIC) for assessment of clinical utility in treatment planning for hydrocephalus. In addition, enhanced images were presented alongside their ground-truth CT counterparts in order to assess whether reconstruction errors caused by the deep learning enhancement routine were acceptable to the evaluators. Results indicate that image resolution and contrast-to-noise ratio between brain and CSF predict the likelihood of an image being characterized as useful for hydrocephalus treatment planning. Deep learning enhancement substantially increases contrast-to-noise ratio improving the apparent likelihood of the image being useful; however, deep learning enhancement introduces structural errors which create a substantial risk of misleading clinical interpretation. We find that images with lower quality than is customarily acceptable can be useful for hydrocephalus treatment planning. Moreover, low quality images may be preferable to images enhanced with deep learning, since they do not introduce the risk of misleading information which could misguide treatment decisions. These findings advocate for new standards in assessing acceptable image quality for clinical use.

Normal childhood brain growth and a universal sex and anthropomorphic relationship to cerebrospinal fluid.

OBJECTIVE: The study of brain size and growth has a long and contentious history, yet normal brain volume development has yet to be fully described. In particular, the normal brain growth and cerebrospinal fluid (CSF) accumulation relationship is critical to characterize because it is impacted in numerous conditions of early childhood in which brain growth and fluid accumulation are affected, such as infection, hemorrhage, hydrocephalus, and a broad range of congenital disorders. The authors of this study aim to describe normal brain volume growth, particularly in the setting of CSF accumulation. METHODS: The authors analyzed 1067 magnetic resonance imaging scans from 505 healthy pediatric subjects from birth to age 18 years to quantify component and regional brain volumes. The volume trajectories were compared between the sexes and hemispheres using smoothing spline ANOVA. Population growth curves were developed using generalized additive models for location, scale, and shape. RESULTS: Brain volume peaked at 10-12 years of age. Males exhibited larger age-adjusted total brain volumes than females, and body size normalization procedures did not eliminate this difference. The ratio of brain to CSF volume, however, revealed a universal age-dependent relationship independent of sex or body size. CONCLUSIONS: These findings enable the application of normative growth curves in managing a broad range of childhood diseases in which cognitive development, brain growth, and fluid accumulation are interrelated.

Brain growth after surgical treatment for infant postinfectious hydrocephalus in Sub-Saharan Africa: 2-year results of a randomized trial.

OBJECTIVE: Hydrocephalus in infants, particularly that with a postinfectious etiology, is a major public health burden in Sub-Saharan Africa. The authors of this study aimed to determine whether surgical treatment of infant postinfectious hydrocephalus in Uganda results in sustained, long-term brain growth and improved cognitive outcome. METHODS: The authors performed a trial at a single center in Mbale, Uganda, involving infants (age < 180 days old) with postinfectious hydrocephalus randomized to endoscopic third ventriculostomy plus choroid plexus cauterization (ETV+CPC; n = 51) or ventriculoperitoneal shunt (VPS; n = 49). After 2 years, they assessed developmental outcome with the Bayley Scales of Infant Development, Third Edition (BSID-III), and brain volume (raw and normalized for age and sex) with CT scans. RESULTS: Eighty-nine infants were assessed for 2-year outcome. There were no significant differences between the two surgical treatment arms in terms of BSID-III cognitive score (p = 0.17) or brain volume (p = 0.36), so they were analyzed together. Raw brain volumes increased between baseline and 2 years (p < 0.001), but this increase occurred almost exclusively in the 1st year (p < 0.001). The fraction of patients with a normal brain volume increased from 15.2% at baseline to 50.0% at 1 year but then declined to 17.8% at 2 years. Substantial normalized brain volume loss was seen in 21.3% patients between baseline and year 2 and in 76.7% between years 1 and 2. The extent of brain growth in the 1st year was not associated with the extent of brain volume changes in the 2nd year. There were significant positive correlations between 2-year brain volume and all BSID-III scores and BSID-III changes from baseline. CONCLUSIONS: In Sub-Saharan Africa, even after successful surgical treatment of infant postinfectious hydrocephalus, early posttreatment brain growth stagnates in the 2nd year. While the reasons for this finding are unclear, it further emphasizes the importance of primary infection prevention and mitigation strategies along with optimizing the child's environment to maximize brain growth potential.

Simultaneous Denoising and Localization Network for Photoacoustic Target Localization.

A significant research problem of recent interest is the localization of targets like vessels, surgical needles, and tumors in photoacoustic (PA) images.To achieve accurate localization, a high photoacoustic signal-to-noise ratio (SNR) is required. However, this is not guaranteed for deep targets, as optical scattering causes an exponential decay in optical fluence with respect to tissue depth. To address this, we develop a novel deep learning method designed to explicitly exhibit robustness to noise present in photoacoustic radio-frequency (RF) data. More precisely, we describe and evaluate a deep neural network architecture consisting of a shared encoder and two parallel decoders. One decoder extracts the target coordinates from the input RF data while the other boosts the SNR and estimates clean RF data. The joint optimization of the shared encoder and dual decoders lends significant noise robustness to the features extracted by the encoder, which in turn enables the network to contain detailed information about deep targets that may be obscured by noise. Additional custom layers and newly proposed regularizers in the training loss function (designed based on observed RF data signal and noise behavior) serve to increase the SNR in the cleaned RF output and improve model performance. To account for depth-dependent strong optical scattering, our network was trained with simulated photoacoustic datasets of targets embedded at different depths inside tissue media of different scattering levels. The network trained on this novel dataset accurately locates targets in experimental PA data that is clinically relevant with respect to the localization of vessels, needles, or brachytherapy seeds. We verify the merits of the proposed architecture by outperforming the state of the art on both simulated and experimental datasets.

Analysis-Synthesis Learning With Shared Features: Algorithms for Histology Image Classification.

OBJECTIVE: The diversity of tissue structure in histopathological images makes feature extraction for classification a challenging task. Dictionary learning within a sparse representation-based classification (SRC) framework has been shown to be successful for feature discovery. However, there exist stiff practical challenges: 1) computational complexity of SRC can be onerous in the decision stage since it involves solving a sparsity constrained optimization problem and often over a large number of image patches; and 2) images from distinct classes continue to share several geometric features. We propose a novel analysis-synthesis model learning with shared features algorithm (ALSF) for classifying such images more effectively. METHODS: In the ALSF, a joint analysis and synthesis learning model is introduced to learn the classifier and the feature extractor at the same time. Unlike SRC, no explicit optimization is needed in the inference phase leading to much reduced computation. Crucially, we introduce the learning of a low-rank shared dictionary and a shared analysis operator, which more accurately represents both similarities and differences in histopathological images from distinct classes. We also develop an extension of ALSF with a sparsity constraint, whose presence or absence facilitates a cost-performance tradeoff. RESULTS: The ALSF is evaluated on three challenging and well-known datasets: 1) spleen tissue images; 2) brain tumor images; and 3) breast cancer tissue dataset, provided by different organizations. CONCLUSION: Experimental results demonstrate both complexity and performance benefits of the ALSF over state-of-the-art alternatives. SIGNIFICANCE: Modeling shared features with appropriate quantitative constraints lead to significantly improved classification in histopathology.

Deep Retinal Image Segmentation with Regularization Under Geometric Priors.

Vessel segmentation of retinal images is a key diagnostic capability in ophthalmology. This problem faces several challenges including low contrast, variable vessel size and thickness, and presence of interfering pathology such as micro-aneurysms and hemorrhages. Early approaches addressing this problem employed hand-crafted filters to capture vessel structures, accompanied by morphological post-processing. More recently, deep learning techniques have been employed with significantly enhanced segmentation accuracy. We propose a novel domain enriched deep network that consists of two components: 1) a representation network that learns geometric features specific to retinal images, and 2) a custom designed computationally efficient residual task network that utilizes the features obtained from the representation layer to perform pixel-level segmentation. The representation and task networks are jointly learned for any given training set. To obtain physically meaningful and practically effective representation filters, we propose two new constraints that are inspired by expected prior structure on these filters: 1) orientation constraint that promotes geometric diversity of curvilinear features, and 2) a data adaptive noise regularizer that penalizes false positives. Multi-scale extensions are developed to enable accurate detection of thin vessels. Experiments performed on three challenging benchmark databases under a variety of training scenarios show that the proposed prior guided deep network outperforms state of the art alternatives as measured by common evaluation metrics, while being more economical in network size and inference time.

Deep MR Brain Image Super-Resolution Using Spatio-Structural Priors.

High resolution Magnetic Resonance (MR) images are desired for accurate diagnostics. In practice, image resolution is restricted by factors like hardware and processing constraints. Recently, deep learning methods have been shown to produce compelling state-of-the-art results for image enhancement/super-resolution. Paying particular attention to desired hi-resolution MR image structure, we propose a new regularized network that exploits image priors, namely a low-rank structure and a sharpness prior to enhance deep MR image super-resolution (SR). Our contributions are then incorporating these priors in an analytically tractable fashion as well as towards a novel prior guided network architecture that accomplishes the super-resolution task. This is particularly challenging for the low rank prior since the rank is not a differentiable function of the image matrix (and hence the network parameters), an issue we address by pursuing differentiable approximations of the rank. Sharpness is emphasized by the variance of the Laplacian which we show can be implemented by a fixed feedback layer at the output of the network. As a key extension, we modify the fixed feedback (Laplacian) layer by learning a new set of training data driven filters that are optimized for enhanced sharpness. Experiments performed on publicly available MR brain image databases and comparisons against existing state-of-the-art methods show that the proposed prior guided network offers significant practical gains in terms of improved SNR/image quality measures. Because our priors are on output images, the proposed method is versatile and can be combined with a wide variety of existing network architectures to further enhance their performance.

Adaptive Transform Domain Image Super-resolution Via Orthogonally Regularized Deep Networks.

Deep learning methods, in particular, trained Convolutional Neural Networks (CNN) have recently been shown to produce compelling results for single image Super-Resolution (SR). Invariably, a CNN is learned to map the Low Resolution (LR) image to its corresponding High Resolution (HR) version in the spatial domain. We propose a novel network structure for learning the SR mapping function in an image transform domain, specifically the Discrete Cosine Transform (DCT). As the first contribution, we show that DCT can be integrated into the network structure as a Convolutional DCT (CDCT) layer. With the CDCT layer, we construct the DCT Deep SR (DCT-DSR) network. We further extend the DCT-DSR to allow the CDCT layer to become trainable (i.e., optimizable). Because this layer represents an image transform, we enforce pairwise orthogonality constraints and newly formulated complexity order constraints on the individual basis functions/filters. This Orthogonally Regularized Deep SR network (ORDSR) simplifies the SR task by taking advantage of image transform domain while adapting the design of transform basis to the training image set. Experimental results show ORDSR achieves state-of-the-art SR image quality with fewer parameters than most of the deep CNN methods. A particular success of ORDSR is in overcoming the artifacts introduced by bicubic interpolation. A key burden of deep SR has been identified as the requirement of generous training LR and HR image pairs; ORSDR exhibits a much more graceful degradation as training size is reduced with significant benefits in the regime of limited training. Analysis of memory and computation requirements confirms that ORDSR can allow for a more efficient network with faster inference.

Robust Alignment for Panoramic Stitching Via an Exact Rank Constraint.

We study the problem of image alignment for panoramic stitching. Unlike most existing approaches that are feature-based, our algorithm works on pixels directly, and accounts for errors across the whole images globally. Technically, we formulate the alignment problem as rank-1 and sparse matrix decomposition over transformed images, and develop an efficient algorithm for solving this challenging non-convex optimization problem. The algorithm reduces to solving a sequence of subproblems, where we analytically establish exact recovery conditions, convergence and optimality, together with convergence rate and complexity. We generalize it to simultaneously align multiple images and recover multiple homographies, extending its application scope toward vast majority of practical scenarios. The experimental results demonstrate that the proposed algorithm is capable of more accurately aligning the images and generating higher quality stitched images than the state-of-the-art methods.

Prior Information Guided Regularized Deep Learning for Cell Nucleus Detection.

Cell nuclei detection is a challenging research topic because of limitations in cellular image quality and diversity of nuclear morphology, i.e., varying nuclei shapes, sizes, and overlaps between multiple cell nuclei. This has been a topic of enduring interest with promising recent success shown by deep learning methods. These methods train convolutional neural networks (CNNs) with a training set of input images and known, labeled nuclei locations. Many such methods are supplemented by spatial or morphological processing. Using a set of canonical cell nuclei shapes, prepared with the help of a domain expert, we develop a new approach that we call shape priors (SPs) with CNNs (SPs-CNN). We further extend the network to introduce an SP layer and then allowing it to become trainable (i.e., optimizable). We call this network as tunable SP-CNN (TSP-CNN). In summary, we present new network structures that can incorporate "expected behavior" of nucleus shapes via two components: learnable layers that perform the nucleus detection and a fixed processing part that guides the learning with prior information. Analytically, we formulate two new regularization terms that are targeted at: 1) learning the shapes and 2) reducing false positives while simultaneously encouraging detection inside the cell nucleus boundary. Experimental results on two challenging datasets reveal that the proposed SP-CNN and TSP-CNN can outperform the state-of-the-art alternatives.

A Visually Interpretable, Dictionary-Based Approach to Imaging-Genomic Modeling, With Low-Grade Glioma as a Case Study.

Radiomics is a rapidly growing field in which sophisticated imaging features are extracted from radiology images to predict clinical outcomes/responses, genetic alterations, and other outcomes relevant to a patient's prognosis or response to therapy. This approach can effectively capture intratumor phenotypic heterogeneity by interrogating the "larger" image field, which is not possible with traditional biopsy procedures that interrogate specific subregions alone. Most models in radiomics derive numerous imaging features (eg, texture, shape, size) from a radiology data set and then learn complex nonlinear hypotheses to solve a given prediction task. This presents the challenge of visual interpretability of radiomic features necessary for effective adoption of radiomic models into the clinical decision-making process. To this end, we employed a dictionary learning approach to derive visually interpretable imaging features relevant to genetic alterations in low-grade gliomas. This model can identify regions of a medical image that potentially influence the prediction process. Using a publicly available data set of magnetic resonance imaging images from patients diagnosed with low-grade gliomas, we demonstrated that the dictionary-based model performs well in predicting 2 biomarkers of interest (1p/19q codeletion and IDH1 mutation). Furthermore, the visual regions (atoms) associated with these dictionaries show association with key molecular pathways implicated in gliomagenesis. Our results show that dictionary learning is a promising approach to obtain insights into the diagnostic process and to potentially aid radiologists in selecting physiologically relevant biopsy locations.

Learning Based Segmentation of CT Brain Images: Application to Postoperative Hydrocephalic Scans.

OBJECTIVE: Hydrocephalus is a medical condition in which there is an abnormal accumulation of cerebrospinal fluid (CSF) in the brain. Segmentation of brain imagery into brain tissue and CSF [before and after surgery, i.e., preoperative (pre-op) versus postoperative (post-op)] plays a crucial role in evaluating surgical treatment. Segmentation of pre-op images is often a relatively straightforward problem and has been well researched. However, segmenting post-op computational tomographic (CT) scans becomes more challenging due to distorted anatomy and subdural hematoma collections pressing on the brain. Most intensity- and feature-based segmentation methods fail to separate subdurals from brain and CSF as subdural geometry varies greatly across different patients and their intensity varies with time. We combat this problem by a learning approach that treats segmentation as supervised classification at the pixel level, i.e., a training set of CT scans with labeled pixel identities is employed. METHODS: Our contributions include: 1) a dictionary learning framework that learns class (segment) specific dictionaries that can efficiently represent test samples from the same class while poorly represent corresponding samples from other classes; 2) quantification of associated computation and memory footprint; and 3) a customized training and test procedure for segmenting post-op hydrocephalic CT images. RESULTS: Experiments performed on infant CT brain images acquired from the CURE Children's Hospital of Uganda reveal the success of our method against the state-of-the-art alternatives. We also demonstrate that the proposed algorithm is computationally less burdensome and exhibits a graceful degradation against a number of training samples, enhancing its deployment potential.

DEEP MR IMAGE SUPER-RESOLUTION USING STRUCTURAL PRIORS.

High resolution magnetic resonance (MR) images are desired for accurate diagnostics. In practice, image resolution is restricted by factors like hardware, cost and processing constraints. Recently, deep learning methods have been shown to produce compelling state of the art results for image superresolution. Paying particular attention to desired hi-resolution MR image structure, we propose a new regularized network that exploits image priors, namely a low-rank structure and a sharpness prior to enhance deep MR image superresolution. Our contributions are then incorporating these priors in an analytically tractable fashion in the learning of a convolutional neural network (CNN) that accomplishes the super-resolution task. This is particularly challenging for the low rank prior, since the rank is not a differentiable function of the image matrix (and hence the network parameters), an issue we address by pursuing differentiable approximations of the rank. Sharpness is emphasized by the variance of the Laplacian which we show can be implemented by a fixed feedback layer at the output of the network. Experiments performed on two publicly available MR brain image databases exhibit promising results particularly when training imagery is limited.

Endoscopic Treatment versus Shunting for Infant Hydrocephalus in Uganda.

BACKGROUND: Postinfectious hydrocephalus in infants is a major health problem in sub-Saharan Africa. The conventional treatment is ventriculoperitoneal shunting, but surgeons are usually not immediately available to revise shunts when they fail. Endoscopic third ventriculostomy with choroid plexus cauterization (ETV-CPC) is an alternative treatment that is less subject to late failure but is also less likely than shunting to result in a reduction in ventricular size that might facilitate better brain growth and cognitive outcomes. METHODS: We conducted a randomized trial to evaluate cognitive outcomes after ETV-CPC versus ventriculoperitoneal shunting in Ugandan infants with postinfectious hydrocephalus. The primary outcome was the Bayley Scales of Infant Development, Third Edition (BSID-3), cognitive scaled score 12 months after surgery (scores range from 1 to 19, with higher scores indicating better performance). The secondary outcomes were BSID-3 motor and language scores, treatment failure (defined as treatment-related death or the need for repeat surgery), and brain volume measured on computed tomography. RESULTS: A total of 100 infants were enrolled; 51 were randomly assigned to undergo ETV-CPC, and 49 were assigned to undergo ventriculoperitoneal shunting. The median BSID-3 cognitive scores at 12 months did not differ significantly between the treatment groups (a score of 4 for ETV-CPC and 2 for ventriculoperitoneal shunting; Hodges-Lehmann estimated difference, 0; 95% confidence interval [CI], -2 to 0; P=0.35). There was no significant difference between the ETV-CPC group and the ventriculoperitoneal-shunt group in BSID-3 motor or language scores, rates of treatment failure (35% and 24%, respectively; hazard ratio, 0.7; 95% CI, 0.3 to 1.5; P=0.24), or brain volume (z score, -2.4 and -2.1, respectively; estimated difference, 0.3; 95% CI, -0.3 to 1.0; P=0.12). CONCLUSIONS: This single-center study involving Ugandan infants with postinfectious hydrocephalus showed no significant difference between endoscopic ETV-CPC and ventriculoperitoneal shunting with regard to cognitive outcomes at 12 months. (Funded by the National Institutes of Health; ClinicalTrials.gov number, NCT01936272 .).

Fast Low-Rank Shared Dictionary Learning for Image Classification.

Despite the fact that different objects possess distinct class-specific features, they also usually share common patterns. This observation has been exploited partially in a recently proposed dictionary learning framework by separating the particularity and the commonality (COPAR). Inspired by this, we propose a novel method to explicitly and simultaneously learn a set of common patterns as well as class-specific features for classification with more intuitive constraints. Our dictionary learning framework is hence characterized by both a shared dictionary and particular (class-specific) dictionaries. For the shared dictionary, we enforce a low-rank constraint, i.e., claim that its spanning subspace should have low dimension and the coefficients corresponding to this dictionary should be similar. For the particular dictionaries, we impose on them the well-known constraints stated in the Fisher discrimination dictionary learning (FDDL). Furthermore, we develop new fast and accurate algorithms to solve the subproblems in the learning step, accelerating its convergence. The said algorithms could also be applied to FDDL and its extensions. The efficiencies of these algorithms are theoretically and experimentally verified by comparing their complexities and running time with those of other well-known dictionary learning methods. Experimental results on widely used image data sets establish the advantages of our method over the state-of-the-art dictionary learning methods.

Sparsity-Based Color Image Super Resolution via Exploiting Cross Channel Constraints.

Sparsity constrained single image super-resolution (SR) has been of much recent interest. A typical approach involves sparsely representing patches in a low-resolution (LR) input image via a dictionary of example LR patches, and then using the coefficients of this representation to generate the high-resolution (HR) output via an analogous HR dictionary. However, most existing sparse representation methods for SR focus on the luminance channel information and do not capture interactions between color channels. In this paper, we extend sparsity-based SR to multiple color channels by taking the color information into account. Edge similarities amongst RGB color bands are exploited as cross channel correlation constraints. These additional constraints lead to a new optimization problem, which is not easily solvable; however, a tractable solution is proposed to solve it efficiently. Moreover, to fully exploit the complementary information among color channels, a dictionary learning method is also proposed specifically to learn color dictionaries that encourage edge similarities. Merits of the proposed method over state of the art are demonstrated both visually and quantitatively using image quality metrics.