Shape-Based Measures Improve Scene Categorization

Converging evidence indicates that deep neural network models that are trained on large datasets are biased toward color and texture information. Humans, on the other hand, can easily recognize objects and scenes from images as well as from bounding contours. Mid-level vision is characterized by the recombination and organization of simple primary features into more complex ones by a set of so-called Gestalt grouping rules. While described qualitatively in the human literature, a computational implementation of these perceptual grouping rules is so far missing. In this article, we contribute a novel set of algorithms for the detection of contour-based cues in complex scenes. We use the medial axis transform (MAT) to locally score contours according to these grouping rules. We demonstrate the benefit of these cues for scene categorization in two ways: (i) Both human observers and CNN models categorize scenes most accurately when perceptual grouping information is emphasized. (ii) Weighting the contours with these measures boosts performance of a CNN model significantly compared to the use of unweighted contours. Our work suggests that, even though these measures are computed directly from contours in the image, current CNN models do not appear to extract or utilize these grouping cues.

Effect of smoke-free policies in military settings on tobacco smoke exposure and smoking behaviour: a systematic review

INTRODUCTION

Smoke-free legislation has been instrumental in reducing secondhand smoke (SHS) exposure in public places. However, the evidence of the impact of institutional smoke-free policies in settings such as healthcare and defence is weaker. Specifically, the literature on the effect of smoke-free policies in military settings has not yet been synthesised.

METHODS

This review aimed to identify, critically appraise and synthesise the available evidence to evaluate the effect of defence smoke-free policies on SHS exposure. Eight electronic databases (eg, EMBASE, MEDLINE) were searched from inception to June 2020. We included English-language studies on smoke-free policies introduced in a defence setting, assessing their impact on SHS exposure (primary outcome) and healthcare utilisation, smoking behaviours and defence efficiency (secondary outcomes). Risk of bias was assessed using ROBINS-I. Synthesis without meta-analysis was conducted using vote counting of direction of effect.

RESULTS

The search retrieved 4503 citations of which eight met inclusion criteria; two controlled and six uncontrolled before-and-after studies. The evidence, although low-quality, from one study indicated reduced SHS exposure following the introduction of a defence smoke-free policy. For secondary outcomes the review found mixed results, with the quit rate being the one outcome favouring smoke-free policies. The cumulative confidence of evidence is uncertain and therefore reliable conclusions cannot be drawn from these studies.

CONCLUSIONS

A research gap exists for high-quality studies on the impact of defence smoke-free policies which should use comparators and, if possible, randomisation. Policy-makers should introduce institutional smoke-free policies in defence settings within an evaluative framework to generate such evidence.

Cardiomyocyte orientation recovery at micrometer scale reveals long-axis fiber continuum in heart walls

Coordinated cardiomyocyte contraction drives the mammalian heart to beat and circulate blood. No consensus model of cardiomyocyte geometrical arrangement exists, due to the limited spatial resolution of whole heart imaging methods and the piecemeal nature of studies based on histological sections. By combining microscopy and computer vision, we produced the first-ever three-dimensional cardiomyocyte orientation reconstruction across mouse ventricular walls at the micrometer scale, representing a gain of three orders of magnitude in spatial resolution. We recovered a cardiomyocyte arrangement aligned to the long-axis direction of the outer ventricular walls. This cellular network lies in a thin shell and forms a continuum with longitudinally arranged cardiomyocytes in the inner walls, with a complex geometry at the apex. Our reconstruction methods can be applied at fine spatial scales to further understanding of heart wall electrical function and mechanics, and set the stage for the study of micron-scale fiber remodeling in heart disease.

Organizing principles of astrocytic nanoarchitecture in the mouse cerebral cortex

Astrocytes are increasingly understood to be important regulators of central nervous system (CNS) function in health and disease; yet, we have little quantitative understanding of their complex architecture. While broad categories of astrocytic structures are known, the discrete building blocks that compose them, along with their geometry and organizing principles, are poorly understood. Quantitative investigation of astrocytic complexity is impeded by the absence of high-resolution datasets and robust computational approaches to analyze these intricate cells. To address this, we produced four ultra-high-resolution datasets of mouse cerebral cortex using serial electron microscopy and developed astrocyte-tailored computer vision methods for accurate structural analysis. We unearthed specific anatomical building blocks, structural motifs, connectivity hubs, and hierarchical organizations of astrocytes. Furthermore, we found that astrocytes interact with discrete clusters of synapses and that astrocytic mitochondria are distributed to lie closer to larger clusters of synapses. Our findings provide a geometrically principled, quantitative understanding of astrocytic nanoarchitecture and point to an unexpected level of complexity in how astrocytes interact with CNS microanatomy.

Predicting histopathology markers of endometrial carcinoma with a quantitative image analysis approach based on spherical harmonics in multiparametric MRI

PURPOSE

Identifying optimal machine learning pipelines for computer-aided diagnosis is key for the development of robust, reproducible, and clinically relevant imaging biomarkers for endometrial carcinoma. The purpose of this study was to introduce the mathematical development of image descriptors computed from spherical harmonics (SPHARM) decompositions as well as the associated machine learning pipeline, and to evaluate their performance in predicting deep myometrial invasion (MI) and histopathological high-grade in preoperative multiparametric magnetic resonance imaging (MRI).

PATIENTS AND METHODS

This retrospective study included 128 women with histopathology-confirmed endometrial carcinomas who underwent 1.5-T MRI before hysterectomy between January 2011 and July 2015. SPHARM descriptors of each tumor were computed on multiparametric MRI images (T2-weighted, diffusion-weighted, dynamic contrast-enhanced-MRI and apparent diffusion coefficient maps). Tensor-based logistic regression was used to classify two-dimensional SPHARM rotationally-invariant descriptors. Head-to-head comparisons with radiomics analyses were performed with DeLong tests with Bonferroni-Holm correction to compare diagnostic performances.

RESULTS

With all MRI contrasts, SPHARM analysis resulted in area under the curve, sensitivity, specificity, and balanced accuracy values of 0.94 (95% confidence interval [CI]: 0.85, 1.00), 100% (95% CI: 100, 100), 74% (95% CI: 51, 92), 87% (95% CI: 78, 98), respectively, for predicting deep MI. For predicting high-grade tumor histology, the corresponding values for the same diagnostic metrics were 0.81 (95% CI: 0.64, 0.90), 93% (95% CI: 67, 100), 63% (95% CI: 45, 79) and 78% (95% CI: 64, 86). The corresponding values achieved via radiomics were 0.92 (95% CI: 0.82, 0.95), 82% (95% CI: 65, 93), 80% (95% CI: 51, 94), 81% (95% CI: 70, 91) for deep MI and 0.72 (95% CI: 0.58, 0.83), 93% (95% CI: 65, 100), 55% (95% CI: 41, 69), 74% (95% CI: 52, 88) for high-grade histology. The diagnostic performance of the SPHARM analysis was not significantly different (P = 0.62) from that of radiomics for predicting deep MI but was significantly higher (P = 0.044) for predicting high-grade histology.

CONCLUSION

The proposed SPHARM analysis yields similar or higher diagnostic performance than radiomics in identifying deep MI and high-grade status in histology-proven endometrial carcinoma.

Scoping Review of Existing Evaluations of Smokeless Tobacco Control Policies: What Is Known About Countries Covered, Level of Jurisdictions, Target Groups Studied, and Instruments Evaluated?

OBJECTIVE

The implementation of smokeless tobacco control policies lags behind those for smoking. This scoping review summarizes the studies that evaluated public policies on smokeless tobacco regulation (SLT) and provides an overview of the jurisdictional level, target groups, and policy instruments.

METHODS

Seven databases were systematically searched for studies reporting on public policies regulating SLT. Two reviewers independently screened all studies. Data extraction was performed using a predefined extraction form. Extraction was replicated for 10% of the identified studies for quality assurance. A narrative synthesis of the included studies was used to analyze and interpret the data. The protocol was published beforehand with the Open Science Foundation (OSF).

RESULTS

Fourty articles comprising 41 studies were included. Most of the studies reported in the articles were conducted in the United States (n = 17) or India (n = 14). Most studies reported outcomes for students (n = 8), retailers/sellers (n = 8), and users/former users (n = 5). The impact of public policies on smokeless tobacco use, in general, was most frequently assessed (n = 9), followed by the impact of taxes (n = 7), product bans (n = 6), sales/advertising bans near educational institutions (n = 4), and health warnings (n = 3) on consumer behavior.

CONCLUSIONS

There are significant gaps in the evaluation of smokeless tobacco regulation studies that need to be filled by further research to understand the observed outcomes. WHO reporting on Framework Convention on Tobacco Control (FCTC) implementation should be linked to studies evaluating smokeless tobacco control measures at all levels of jurisdictions and in countries not members of the WHO FCTC or do not provide data.

IMPLICATION

Large gaps in the evaluation of SLT control policies exist. For some countries, WHO FCTC evaluations are available for different levels of jurisdictions. In countries with a strong federal structure, there is a lack of data beyond the national level to provide a more detailed look at compliance, indirect effects, or implementation gaps. More research is needed at all levels of jurisdictions, which add to the work of the WHO to understand what works for which target group, how the different levels of jurisdiction interact, how the real-world context can be incorporated, and what indirect effects may occur.

Neural correlates of local parallelism during naturalistic vision

Human observers can rapidly perceive complex real-world scenes. Grouping visual elements into meaningful units is an integral part of this process. Yet, so far, the neural underpinnings of perceptual grouping have only been studied with simple lab stimuli. We here uncover the neural mechanisms of one important perceptual grouping cue, local parallelism. Using a new, image-computable algorithm for detecting local symmetry in line drawings and photographs, we manipulated the local parallelism content of real-world scenes. We decoded scene categories from patterns of brain activity obtained via functional magnetic resonance imaging (fMRI) in 38 human observers while they viewed the manipulated scenes. Decoding was significantly more accurate for scenes containing strong local parallelism compared to weak local parallelism in the parahippocampal place area (PPA), indicating a central role of parallelism in scene perception. To investigate the origin of the parallelism signal we performed a model-based fMRI analysis of the public BOLD5000 dataset, looking for voxels whose activation time course matches that of the locally parallel content of the 4916 photographs viewed by the participants in the experiment. We found a strong relationship with average local symmetry in visual areas V1-4, PPA, and retrosplenial cortex (RSC). Notably, the parallelism-related signal peaked first in V4, suggesting V4 as the site for extracting paralleism from the visual input. We conclude that local parallelism is a perceptual grouping cue that influences neuronal activity throughout the visual hierarchy, presumably starting at V4. Parallelism plays a key role in the representation of scene categories in PPA.

Improved Detection of Chronic Obstructive Pulmonary Disease at Chest CT Using the Mean Curvature of Isophotes

PURPOSE

To determine if the mean curvature of isophotes (MCI), a standard computer vision technique, can be used to improve detection of chronic obstructive pulmonary disease (COPD) at chest CT.

MATERIALS AND METHODS

In this retrospective study, chest CT scans were obtained in 243 patients with COPD and 31 controls (among all 274: 151 women [mean age, 70 years; range, 44-90 years] and 123 men [mean age, 71 years; range, 29-90 years]) from two community practices between 2006 and 2019. A convolutional neural network (CNN) architecture was trained on either CT images or CT images transformed through the MCI algorithm. Separately, a linear classification based on a single feature derived from the MCI computation (called hMCI1) was also evaluated. All three models were evaluated with cross-validation, using precision-macro and recall-macro metrics, that is, the mean of per-class precision and recall values, respectively (the latter being equivalent to balanced accuracy).

RESULTS

Linear classification based on hMCI1 resulted in a higher recall-macro relative to the CNN trained and applied on CT images (0.85 [95% CI: 0.84, 0.86] vs 0.77 [95% CI: 0.75, 0.79]) but with a similar reduction in precision-macro (0.66 [95% CI: 0.65, 0.67] vs 0.77 [95% CI: 0.75, 0.79]). The CNN model trained and applied on MCI-transformed images had a higher recall-macro (0.85 [95% CI: 0.83, 0.87] vs 0.77 [95% CI: 0.75, 0.79]) and precision-macro (0.85 [95% CI: 0.83, 0.87] vs 0.77 [95% CI: 0.75, 0.79]) relative to the CNN trained and applied on CT images.

CONCLUSION

The MCI algorithm may be valuable toward the automated detection and diagnosis of COPD on chest CT scans as part of a CNN-based pipeline or with stand-alone features.Keywords: Chronic Obstructive Pulmonary Disease, Quantification, Lung, CT Supplemental material is available for this article. See also the invited commentary by Vannier in this issue.© RSNA, 2021.

Spine and Individual Vertebrae Segmentation in Computed Tomography Images Using Geometric Flows and Shape Priors

The surgical treatment of injuries to the spine often requires the placement of pedicle screws. To prevent damage to nearby blood vessels and nerves, the individual vertebrae and their surrounding tissue must be precisely localized. To aid surgical planning in this context we present a clinically applicable geometric flow based method to segment the human spinal column from computed tomography (CT) scans. We first apply anisotropic diffusion and flux computation to mitigate the effects of region inhomogeneities and partial volume effects at vertebral boundaries in such data. The first pipeline of our segmentation approach uses a region-based geometric flow, requires only a single manually identified seed point to initiate, and runs efficiently on a multi-core central processing unit (CPU). A shape-prior formulation is employed in a separate second pipeline to segment individual vertebrae, using both region and boundary based terms to augment the initial segmentation. We validate our method on four different clinical databases, each of which has a distinct intensity distribution. Our approach obviates the need for manual segmentation, significantly reduces inter- and intra-observer differences, runs in times compatible with use in a clinical workflow, achieves Dice scores that are comparable to the state of the art, and yields precise vertebral surfaces that are well within the acceptable 2 mm mark for surgical interventions.

Ice Core Science Meets Computer Vision: Challenges and Perspectives

Polar ice cores play a central role in studies of the earth’s climate system through natural archives. A pressing issue is the analysis of the oldest, highly thinned ice core sections, where the identification of paleoclimate signals is particularly challenging. For this, state-of-the-art imaging by laser-ablation inductively-coupled plasma mass spectrometry (LA-ICP-MS) has the potential to be revolutionary due to its combination of micron-scale 2D chemical information with visual features. However, the quantitative study of record preservation in chemical images raises new questions that call for the expertise of the computer vision community. To illustrate this new inter-disciplinary frontier, we describe a selected set of key questions. One critical task is to assess the paleoclimate significance of single line profiles along the main core axis, which we show is a scale-dependent problem for which advanced image analysis methods are critical. Another important issue is the evaluation of post-depositional layer changes, for which the chemical images provide rich information. Accordingly, the time is ripe to begin an intensified exchange between the two scientific communities of computer vision and ice core science. The collaborative building of a new framework for investigating high-resolution chemical images with automated image analysis techniques will also benefit the already wide-spread application of laser-ablation inductively-coupled plasma mass spectrometry chemical imaging in the geosciences.

Diurnal variability of fine-particulate pollution concentrations: data from 14 low- and middle-income countries

BACKGROUND: Scientific understanding of indoor air pollution is predominately based on research carried out in cities in high-income countries (HICs). Less is known about how pollutant concentrations change over the course of a typical day in cities in low- and middle-income countries (LMICs).OBJECTIVE: To understand how concentrations of fine particulate matter smaller than 2.5 microns in diameter (PM_2.5) change over the course of the day outdoors (across a range of countries) and indoors (using measurements from Dhaka, Bangladesh).DESIGN: Data on PM_2.5 concentrations were gathered from 779 households in Dhaka as part of the MCLASS II (Muslim Communities Learning About Second-hand Smoke in Bangladesh) project, and compared to outdoor PM_2.5 concentrations to determine the temporal variation in exposure to air pollution. Hourly PM_2.5 data from 23 cities in 14 LMICs, as well as London (UK), Paris (France) and New York (NY, USA), were extracted from publicly available sources for comparison.RESULTS: PM_2.5 in homes in Dhaka demonstrated a similar temporal pattern to outdoor measurements, with greater concentrations at night than in the afternoon. This pattern was also evident in 19 of 23 LMIC cities.CONCLUSION: PM_2.5 concentrations are greater at night than during the afternoon in homes in Dhaka. Diurnal variations in PM_2.5 in LMICs is substantial and greater than in London, Paris or New York. This has implications for public health community approaches to health effects of air pollution in LMICs.

Association of Plasmodium berghei With the Apical Domain of Hepatocytes Is Necessary for the Parasite's Liver Stage Development

Plasmodium parasites undergo a dramatic transformation during the liver stage of their life cycle, amplifying over 10,000-fold inside infected hepatocytes within a few days. Such a rapid growth requires large-scale interactions with, and manipulations of, host cell functions. Whereas hepatocyte polarity is well-known to be critical for liver function, little is presently known about its involvement during the liver stage of Plasmodium development. Apical domains of hepatocytes are critical components of their polarity machinery and constitute the bile canalicular network, which is central to liver function. Here, we employed high resolution 3-D imaging and advanced image analysis of Plasmodium-infected liver tissues to show that the parasite associates preferentially with the apical domain of hepatocytes and induces alterations in the organization of these regions, resulting in localized changes in the bile canalicular architecture in the liver tissue. Pharmacological perturbation of the bile canalicular network by modulation of AMPK activity reduces the parasite's association with bile canaliculi and arrests the parasite development. Our findings using Plasmodium-infected liver tissues reveal a host-Plasmodium interaction at the level of liver tissue organization. We demonstrate for the first time a role for bile canaliculi, a central component of the hepatocyte polarity machinery, during the liver stage of Plasmodium development.

Local contour symmetry facilitates scene categorization

People are able to rapidly categorize briefly flashed images of real-world environments, even when they are reduced to line drawings. This setting allows for the study of time-limited perceptual grouping processes in the human visual system that are applicable to line drawings. Previous work (Wilder, Dickinson, Jepson, & Walther, 2018) showed that standard local features of individual contours, or junctions between contours, do not account for this rapid classification ability but, rather, the relative placement of these contours appeared to be important. Here we provide strong support for this observation by demonstrating that local ribbon symmetry between neighboring pairs of contours facilitates the categorization of complex real-world environments. To this end, we introduce a novel computational approach, based on the medial axis transform, for measuring the degree of local ribbon symmetry in a line drawing. We use this measure to separate the contour pixels for a given scene into the most ribbon symmetric half and the least ribbon symmetric half. We then show human observers the resulting half-images in a rapid-categorization experiment. Our results demonstrate that local ribbon symmetry facilitates the categorization of complex real-world environments. This is the first study of the role of local symmetry in inter-contour grouping for human scene classification. We conclude that local ribbon symmetry appears to play an important role in jump-starting the grouping of image content into meaningful units, even in flashed presentations.

Statistical shape modeling of the left ventricle: myocardial infarct classification challenge

Statistical shape modeling is a powerful tool for visualizing and quantifying geometric and functional patterns of the heart. After myocardial infarction (MI), the left ventricle typically remodels in response to physiological challenges. Several methods have been proposed in the literature to describe statistical shape changes. Which method best characterizes left ventricular remodeling after MI is an open research question. A better descriptor of remodeling is expected to provide a more accurate evaluation of disease status in MI patients. We therefore designed a challenge to test shape characterization in MI given a set of three-dimensional left ventricular surface points. The training set comprised 100 MI patients, and 100 asymptomatic volunteers (AV). The challenge was initiated in 2015 at the Statistical Atlases and Computational Models of the Heart workshop, in conjunction with the MICCAI conference. The training set with labels was provided to participants, who were asked to submit the likelihood of MI from a different (validation) set of 200 cases (100 AV and 100 MI). Sensitivity, specificity, accuracy and area under the receiver operating characteristic curve were used as the outcome measures. The goals of this challenge were to (1) establish a common dataset for evaluating statistical shape modeling algorithms in MI, and (2) test whether statistical shape modeling provides additional information characterizing MI patients over standard clinical measures. Eleven groups with a wide variety of classification and feature extraction approaches participated in this challenge. All methods achieved excellent classification results with accuracy ranges from 0.83 to 0.98. The areas under the receiver operating characteristic curves were all above 0.90. Four methods showed significantly higher performance than standard clinical measures. The dataset and software for evaluation are available from the Cardiac Atlas Project website1.

Involving mosques in health promotion programmes: a qualitative exploration of the MCLASS intervention on smoking in the home

Second-hand smoke (SHS) exposure is high among UK Bangladeshi and Pakistani populations, reflecting higher male smoking prevalence and fewer home smoking restrictions than the general population. The Muslim Communities Learning About Second-hand Smoke (MCLASS) study explored the feasibility and acceptability of implementing SHS education in 14 UK mosques. Religious teachers (RTs) in seven intervention mosques were trained and provided with a culturally appropriate educational package. After the intervention, mosque leaders, RTs and congregants' experiences and perceptions of the intervention were explored through interviews and focus group discussions. Delivery of the intervention varied across mosques. Facilitators and barriers included: mosque diversity (congregation size, organizational structure, educational activities, women's role and involvement); degree of trust between researchers and personnel; and views on SHS. Most participants thought mosques' involvement in SHS health promotion was appropriate, but the perceived importance of SHS differed. We found that a health promotion programme delivered within Islamic religious settings that engages RTs in the process of facilitation, can be acceptable and feasible, but care must be taken to explore the culture and ethos of the institution, including its organizational structure, management committee, RTs and congregation.

Subject-Specific Longitudinal Shape Analysis by Coupling Spatiotemporal Shape Modeling with Medial Analysis

Modeling subject-specific shape change is one of the most important challenges in longitudinal shape analysis of disease progression. Whereas anatomical change over time can be a function of normal aging; anatomy can also be impacted by disease related degeneration. Shape changes to anatomy may also be affected by external structural changes from neighboring structures, which may cause non-linear pose variations. In this paper, we propose a framework to analyze disease related shape changes by coupling extrinsic modeling of the ambient anatomical space via spatiotemporal deformations with intrinsic shape properties from medial surface analysis. We compare intrinsic shape properties of a subject-specific shape trajectory to a normative 4D shape atlas representing normal aging to separately quantify shape changes related to disease. The spatiotemporal shape modeling establishes inter/intra subject anatomical correspondence, which in turn enables comparisons between subjects and the 4D shape atlas, and also quantitative analysis of disease related shape change. The medial surface analysis captures intrinsic shape properties related to local patterns of deformation. The proposed framework simultaneously models extrinsic longitudinal shape changes in the ambient anatomical space, as well as intrinsic shape properties to give localized measurements of degeneration. Six high risk subjects and six controls are randomly sampled from a Huntington's disease image database for quantitative and qualitative comparison.

Maurer-Cartan Forms for Fields on Surfaces: Application to Heart Fiber Geometry

We study the space of first order models of smooth frame fields using the method of moving frames. By exploiting the Maurer-Cartan matrix of connection forms we develop geometrical embeddings for frame fields which lie on spherical, ellipsoidal and generalized helicoid surfaces. We design methods for optimizing connection forms in local neighborhoods and apply these to a statistical analysis of heart fiber geometry, using diffusion magnetic resonance imaging. This application of moving frames corroborates and extends recent characterizations of muscle fiber orientation in the heart wall, but also provides for a rich geometrical interpretation. In particular, we can now obtain direct local measurements of the variation of the helix and transverse angles, of fiber fanning and twisting, and of the curvatures of the heart wall in which these fibers lie.

Predictors of cessation in smokers suspected of TB: Secondary analysis of data from a cluster randomized controlled trial

BACKGROUND

Smoking cessation services are rarely found within health services in low income countries. Given the interactions between Tuberculosis (TB) and tobacco, including cessation support within TB programs offers a promising cost-effective solution. We conducted secondary analysis of data from a cluster randomized controlled trial of smoking cessation in health centers in Pakistan to identify predictors of continuous and short-term abstinence in smokers suspected of TB using cigarettes or hookah.

METHODS

Predictor variables of those continuously abstinent at 5 and 25 weeks post quit-date (continuous abstinence) and those abstinent only at 5 weeks (short-term abstinence) were compared with those who continued smoking and with each other. Self-reported abstinence at both time points was confirmed biochemically.

RESULTS

Data obtained from 1955 trial participants were analyzed. The factors that predicted continued smoking when compared to continuous abstinence were: being older RR 0.97 (0.95 to 0.98), smoking higher quantities of tobacco RR 0.975 (0.97 to 0.98) and sharing a workplace with other smokers RR 0.88 (0.77 to 0.99). Those with a confirmed TB diagnosis were more likely to remain continuously abstinent than those without RR 1.27 (1.10-1.47).

CONCLUSIONS

Those diagnosed with TB are more likely to be abstinent than those diagnosed with other respiratory conditions. Beyond this, predictors of continued smoking in Pakistan are similar to those in high income contexts. Taking advantage of the 'teachable moment' that a TB diagnosis provides is an efficient means for resource-poor TB programs in low income settings to increase tobacco cessation and improve health outcomes.

Tuberculosis patients learning about second-hand smoke (TBLASS): results of a pilot randomised controlled trial

BACKGROUND

Passive smoking is associated with tuberculosis (TB). Measures are required to protect non-smoking TB patients from second-hand smoke (SHS).

SETTING

We developed a behavioural intervention to encourage TB patients to implement smoking restrictions at home in Pakistan.

OBJECTIVES

To assess the likelihood of such an intervention being successful and to inform a definitive trial in future.

DESIGN

This was a pilot randomised controlled trial in which non-smoking TB patients were randomised to receive either individual-based support or individual-based support combined with family reminders. We recruited patients living with at least one smoker in their homes. Our primary outcome was urinary cotinine level as a measure of SHS exposure 2 months post-randomisation.

RESULTS

Of 273 patients assessed for eligibility, 150 (56%) patients were recruited and all but one were retained throughout the trial. A statistically significant absolute reduction was observed in non-smoking participants' exposure to SHS based on urinary cotinine levels in both Arm 1 (71%, 95%CI 61-79) and Arm 2 (76%, 95%CI 67-83) between baseline and follow-up at 2 months.

CONCLUSIONS

The recruitment and retention rates for trial participants make it feasible to conduct a definitive trial in future. The observed effect size makes it worthwhile to conduct such a trial.

Moving Frames for Heart Fiber Reconstruction

The method of moving frames provides powerful geometrical tools for the analysis of smoothly varying frame fields. However, in the face of missing measurements, a reconstruction problem arises, one that is largely unexplored for 3D frame fields. Here we consider the particular example of reconstructing impaired cardiac diffusion magnetic resonance imaging (dMRI) data. We combine moving frame analysis with a diffusion inpainting scheme that incorporates rule-based priors. In contrast to previous reconstruction methods, this new approach uses comprehensive differential descriptors for cardiac fibers, and is able to fully recover their orientation. We demonstrate the superior performance of this approach in terms of error of fit when compared to alternate methods. We anticipate that these tools could find application in clinical settings, where damaged heart tissue needs to be replaced or repaired, and for generating dense fiber volumes in electromechanical modelling of the heart.

Beyond crossing fibers: bootstrap probabilistic tractography using complex subvoxel fiber geometries

Diffusion magnetic resonance imaging fiber tractography is a powerful tool for investigating human white matter connectivity in vivo. However, it is prone to false positive and false negative results, making interpretation of the tractography result difficult. Optimal tractography must begin with an accurate description of the subvoxel white matter fiber structure, includes quantification of the uncertainty in the fiber directions obtained, and quantifies the confidence in each reconstructed fiber tract. This paper presents a novel and comprehensive pipeline for fiber tractography that meets the above requirements. The subvoxel fiber geometry is described in detail using a technique that allows not only for straight crossing fibers but for fibers that curve and splay. This technique is repeatedly performed within a residual bootstrap statistical process in order to efficiently quantify the uncertainty in the subvoxel geometries obtained. A robust connectivity index is defined to quantify the confidence in the reconstructed connections. The tractography pipeline is demonstrated in the human brain.

Smokeless tobacco consumption in a multi-ethnic community in Pakistan: a cross-sectional study

Smokeless tobacco is commonly used in south Asia. In addition to causing oral and pharyngeal cancers, its harmful effects are comparable to smoking tobacco. A cross-sectional survey with systematic sampling was conducted in 2010-2011 to investigate smokeless tobacco use in a multi-ethnic, semi-urban population in Islamabad, Pakistan (n = 2030). The prevalence of smokeless tobacco use was 16.0% (21.6% among males and 8.8% among females); 51.7% of smokeless tobacco users were also cigarette smokers. The rate of smokeless tobacco use was comparatively high among Pakhtun males (38.2%) and Sindhi females (22.4%). The associations between smokeless tobacco use and ethnicity, age group, income level and cigarette smoking were statistically significant among male smokeless tobacco users. Of the sample 41.4% (840/2030) had inadequate knowledge about the health problems associated with smokeless tobacco. Appropriate interventions are needed to raise awareness of the health risks and to prevent smokeless tobacco use.

3D stochastic completion fields for mapping connectivity in diffusion MRI

The 2D stochastic completion field algorithm, introduced by Williams and Jacobs [1], [2], uses a directional random walk to model the prior probability of completion curves in the plane. This construct has had a powerful impact in computer vision, where it has been used to compute the shapes of likely completion curves between edge fragments in visual imagery. Motivated by these developments, we extend the algorithm to 3D, using a spherical harmonics basis to achieve a rotation invariant computational solution to the Fokker-Planck equation describing the evolution of the probability density function underlying the model. This provides a principled way to compute 3D completion patterns and to derive connectivity measures for orientation data in 3D, as arises in 3D tracking, motion capture, and medical imaging. We demonstrate the utility of the approach for the particular case of diffusion magnetic resonance imaging, where we derive connectivity maps for synthetic data, on a physical phantom and on an in vivo high angular resolution diffusion image of a human brain.

Diffeomorphic spectral matching of cortical surfaces

Accurate matching of cortical surfaces is necessary in many neuroscience applications. In this context diffeomorphisms are often sought, because they facilitate further statistical analysis and atlas building. Present methods for computing diffeomorphisms are based on optimizing flows or on inflating surfaces to a common template, but they are often computationally expensive. It typically takes several hours on a conventional desktop computer to match a single pair of cortical surfaces having a few hundred thousand vertices. We propose a very fast alternative based on an application of spectral graph theory on a novel association graph. Our symmetric approach can generate a diffeomorphic correspondence map within a few minutes on high-resolution meshes while avoiding the sign and multiplicity ambiguities of conventional spectral matching methods. The eigenfunctions are shared between surfaces and provide a smooth parameterization of surfaces. These properties are exploited to compute differentials on highly folded cortical surfaces. Diffeomorphisms can thus be verified and invalid surface folding detected. Our method is demonstrated to attain a vertex accuracy that is at least as good as that of FreeSurfer and Spherical Demons but in only a fraction of their processing time. As a practical experiment, we construct an unbiased atlas of cortical surfaces with a speed several orders of magnitude faster than current methods.

Medial spheres for shape approximation

We study the problem of approximating a 3D solid with a union of overlapping spheres. In comparison with a state-of-the-art approach, our method offers more than an order of magnitude speedup and achieves a tighter approximation in terms of volume difference with the original solid while using fewer spheres. The spheres generated by our method are internal and tangent to the solid's boundary, which permits an exact error analysis, fast updates under local feature size preserving deformation, and conservative dilation. We show that our dilated spheres offer superior time and error performance in approximate separation distance tests than the state-of-the-art method for sphere set approximation for the class of (σ,θ)-fat solids. We envision that our sphere-based approximation will also prove useful for a range of other applications, including shape matching and shape segmentation.

Apparent intravoxel fibre population dispersion (FPD) using spherical harmonics

The vast majority of High Angular Resolution Diffusion Imaging (HARDI) modeling methods recover networks of neuronal fibres, using a heuristic extraction of their local orientation. In this paper, we present a method for computing the apparent intravoxel Fibre Population Dispersion (FPD), which conveys the manner in which distinct fibre populations are partitioned within the same voxel. We provide a statistical analysis, without any prior assumptions on the number or size of these fibre populations, using an analytical formulation of the diffusion signal autocorrelation function in the spherical harmonics basis. We also propose to extract features of the FPD obtained in the group of rotations, using several metrics based on unit quaternions. We show results on simulated data and on physical phantoms, that demonstrate the effectiveness of the FPD to reveal regions with crossing tracts, in contrast to the standard anisotropy measures.

Removal of partial occlusion from single images

This paper examines large partial occlusions in an image which occur near depth discontinuities when the foreground object is severely out of focus. We model these partial occlusions using matting, with the alpha value determined by the convolution of the blur kernel with a pinhole projection of the occluder. The main contribution is a method for removing the image contribution of the foreground occluder in regions of partial occlusion, which improves the visibility of the background scene. The method consists of three steps. First, the region of complete occlusion is estimated using a curve evolution method. Second, the alpha value at each pixel in the partly occluded region is estimated. Third, the intensity contribution of the foreground occluder is removed in regions of partial occlusion. Experiments demonstrate the method's ability to remove the effects of partial occlusion in single images with minimal user input.

Rotation invariant completion fields for mapping diffusion MRI connectivity

Partial differential equations have been successfully used for fibre tractography and for mapping connectivity indices in the brain. However, the current implementation of methods which require 3D orientation to be tracked can suffer from serious shortcomings when invariance to 3D rotation is desired. In this paper we focus on the 3D stochastic completion field and introduce a new methodology to solve the underlying PDE in a manner that achieves rotation invariance. The key idea is to use spherical harmonics to solve the Fokker-Planck equation representing the evolution of the probability density function of a 3D directional random walk. We validate the new approach by presenting improved connectivity indices on synthetic data, on the MICCAI 2009 Fibre Cup phantom and on a biological phantom comprised of two rat spinal chords in a crossing configuration.

Smoke-free homes: an intervention to reduce second-hand smoke exposure in households

BACKGROUND

Second-hand tobacco smoke is a serious health hazard. We tested the fidelity and feasibility of the Smoke-Free Homes (SFH) intervention and looked for preliminary evidence of its effectiveness in imposing smoking restrictions in homes in Pakistan.

METHODS

SFH was piloted and adapted for Pakistan. The adapted SFH intervention was then delivered to primary schoolchildren, community leaders and health professionals in a semi-rural Union Council. We carried out a survey before and after the intervention to assess adult smoking behaviour and restrictions at homes. We also carried out focus group discussions with stakeholders to determine the appropriateness and acceptability of the intervention.

RESULTS

We found the adapted SFH intervention feasible and appropriate in a typical semi-rural setting in Pakistan. The proportion of smoke-free homes increased from 43% (95%CI 37.4-48.2) to 85% (95%CI 80.9-89.2) after the intervention. The number of households with at least one smoker decreased from 57.5% (95%CI 52.1-62.9) to 38.4% (95%CI 32.7-44.1). There was a reduction in self-reported adult smoking prevalence from 44% (95%CI 39-48) to 28% (95%CI 24-33) in males.

CONCLUSION

SFH has the potential to influence adult smoking behaviour in households. This approach needs to be further evaluated to establish its effectiveness and cost-effectiveness and to ascertain its long-term sustainability.

TurboPixels: fast superpixels using geometric flows

We describe a geometric-flow-based algorithm for computing a dense oversegmentation of an image, often referred to as superpixels. It produces segments that, on one hand, respect local image boundaries, while, on the other hand, limiting undersegmentation through a compactness constraint. It is very fast, with complexity that is approximately linear in image size, and can be applied to megapixel sized images with high superpixel densities in a matter of minutes. We show qualitative demonstrations of high-quality results on several complex images. The Berkeley database is used to quantitatively compare its performance to a number of oversegmentation algorithms, showing that it yields less undersegmentation than algorithms that lack a compactness constraint while offering a significant speedup over N-cuts, which does enforce compactness.

Oriented morphometry of folds on surfaces

The exterior surface of the brain is characterized by a juxtaposition of crests and troughs that together form a folding pattern. The majority of the deformations that occur in the normal course of adult human development result in folds changing their length or width. Current statistical shape analysis methods cannot easily discriminate between these two cases. Using discrete exterior calculus and Tikhonov regularization, we develop a method to estimate a dense orientation fiel in the tangent space of a surface described by a triangulated mesh, in the direction of its folds. We then use this orientation field to distinguish between shape differences in the direction parallel to folds and those in the direction across them. We test the method quantitatively on synthetic data and qualitatively on a database consisting of segmented cortical surfaces of 92 healthy subjects and 97 subjects with Alzheimer's disease. The method estimates the correct fold directions and also indicates that the healthy and diseased subjects are distinguished by shape differences that are in the direction perpendicular to the underlying hippocampi, a finding which is consistent with the neuroscientific literature. These results demonstrate the importance of direction specific computational methods for shape analysis.

Mathematical methods for diffusion MRI processing

In this article, we review recent mathematical models and computational methods for the processing of diffusion Magnetic Resonance Images, including state-of-the-art reconstruction of diffusion models, cerebral white matter connectivity analysis, and segmentation techniques. We focus on Diffusion Tensor Images (DTI) and Q-Ball Images (QBI).

A geometric flow for segmenting vasculature in proton-density weighted MRI

Modern neurosurgery takes advantage of magnetic resonance images (MRI) of a patient's cerebral anatomy and vasculature for planning before surgery and guidance during the procedure. Dual echo acquisitions are often performed that yield proton-density (PD) and T2-weighted images to evaluate edema near a tumor or lesion. In this paper we develop a novel geometric flow for segmenting vasculature in PD images, which can also be applied to the easier cases of MR angiography data or Gadolinium enhanced MRI. Obtaining vasculature from PD data is of clinical interest since the acquisition of such images is widespread, the scanning process is non-invasive, and the availability of vessel segmentation methods could obviate the need for an additional angiographic or contrast-based sequence during preoperative imaging. The key idea is to first apply Frangi's vesselness measure [Frangi, A., Niessen, W., Vincken, K.L., Viergever, M.A., 1998. Multiscale vessel enhancement filtering. In: International Conference on Medical Image Computing and Computer Assisted Intervention, vol. 1496 of Lecture Notes in Computer Science, pp. 130-137] to find putative centerlines of tubular structures along with their estimated radii. This measure is then distributed to create a vector field which allows the flux maximizing flow algorithm of Vasilevskiy and Siddiqi [Vasilevskiy, A., Siddiqi, K., 2002. Flux maximizing geometric flows. IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (12), 1565-1578] to be applied to recover vessel boundaries. We carry out a qualitative validation of the approach on PD, MR angiography and Gadolinium enhanced MRI volumes and suggest a new way to visualize the segmentations in 2D with masked projections. We validate the approach quantitatively on a single-subject data set consisting of PD, phase contrast (PC) angiography and time of flight (TOF) angiography volumes, with an expert segmented version of the TOF volume viewed as the ground truth. We then validate the approach quantitatively on 19 PD data sets from a new digital brain phantom, with semi-automatically obtained labels from the corresponding angiography volumes viewed as ground truth. A significant finding is that both for the single-subject and multi-subject studies, 90% or more of the vasculature in the ground truth segmentation is recovered from the automatic segmentation of the other volumes.

Labeling of ambiguous subvoxel fibre bundle configurations in high angular resolution diffusion MRI

Whereas high angular resolution reconstruction methods for diffusion MRI can estimate multiple dominant fibre orientations within a single imaging voxel, they are fundamentally limited in certain cases of complex subvoxel fibre structures, resulting in ambiguous local orientation distribution functions. In this article we address the important problem of disambiguating such complex subvoxel fibre tract configurations, with the purpose of improving the performance of fibre tractography. We do so by extending a curve inference method to distinguish between the cases of curving and fanning fibre bundles using differential geometric estimates in a local neighbourhood. The key benefit of this method is the inference of curves, instead of only fibre orientations, to model the underlying fibre bundles. This in turn allows distinct fibre geometries that contain nearly identical sets of fibre orientations at a voxel, to be distinguished from one another. Experimental results demonstrate the ability of the method to successfully label voxels into one of the above categories and improve the performance of a fibre-tracking algorithm.

Validation of vessel-based registration for correction of brain shift

The displacement and deformation of brain tissue is a major source of error in image-guided neurosurgery systems. We have designed and implemented a method to detect and correct brain shift using pre-operative MR images and intraoperative Doppler ultrasound data and present its validation with both real and simulated data. The algorithm uses segmented vessels from both modalities, and estimates the deformation using a modified version of the iterative closest point (ICP) algorithm. We use the least trimmed squares (LTS) to reduce the number of outliers in the point matching procedure. These points are used to drive a thin-plate spline transform to achieve non-linear registration. Validation was completed in two parts. First, the technique was tested and validated using realistic simulations where the results were compared to the known deformation. The registration technique recovered 75% of the deformation in the region of interest accounting for deformations as large as 20 mm. Second, we performed a PVA-cryogel phantom study where both MR and ultrasound images of the phantom were obtained for three different deformations. The registration results based on MR data were used as a gold standard to evaluate the performance of the ultrasound based registration. On average, deformations of 7.5 mm magnitude were corrected to within 1.6 mm for the ultrasound based registration and 1.07 mm for the MR based registration.

Improving sputum microscopy services for the diagnosis of tuberculosis in Peru and Bolivia

SETTING

Sixteen primary care health centres in Peru and Bolivia.

OBJECTIVES

To assess the utilisation of microscopy services in Peru and Bolivia and determine if clinical audit, a quality improvement tool, improves the utilisation of these services.

DESIGN

We estimated the percentage of patients with suspected tuberculosis (TB) in whom sputum microscopy was effectively utilised in Peru and Bolivia over two 6-month periods before and after a clinical audit intervention that included standards setting, measuring clinical performance and feedback.

RESULTS

Before the intervention, only 31% (95%CI 27-35) of TB suspects were assessed with sputum microscopy in Peru. In Bolivia, 30% (95%CI 25-35) underwent at least two sputum microscopy examinations. After clinical audit, the availability of sputum microscopy results improved by respectively 7% (95%CI 1-12, P < 0.05) and 23% (95%CI 15-30, P < 0.05) over 2 years in Peru and Bolivia.

CONCLUSIONS

Despite World Health Organization recommendations that all TB suspects should undergo sputum microscopy before treatment, results are available for further assessment for only one third. This is a potentially serious obstacle to TB case detection. Clinical audit can bring some improvement. We recommend regular monitoring of effective utilisation of microscopy services and investigations to ascertain organisational and structural issues in their uptake and use.