Dataset Information

On Statistical Analysis of Neuroimages with Imperfect Registration.

ABSTRACT: A variety of studies in neuroscience/neuroimaging seek to perform statistical inference on the acquired brain image scans for diagnosis as well as understanding the pathological manifestation of diseases. To do so, an important first step is to register (or co-register) all of the image data into a common coordinate system. This permits meaningful comparison of the intensities at each voxel across groups (e.g., diseased versus healthy) to evaluate the effects of the disease and/or use machine learning algorithms in a subsequent step. But errors in the underlying registration make this problematic, they either decrease the statistical power or make the follow-up inference tasks less effective/accurate. In this paper, we derive a novel algorithm which offers immunity to local errors in the underlying deformation field obtained from registration procedures. By deriving a deformation invariant representation of the image, the downstream analysis can be made more robust as if one had access to a (hypothetical) far superior registration procedure. Our algorithm is based on recent work on scattering transform. Using this as a starting point, we show how results from harmonic analysis (especially, non-Euclidean wavelets) yields strategies for designing deformation and additive noise invariant representations of large 3-D brain image volumes. We present a set of results on synthetic and real brain images where we achieve robust statistical analysis even in the presence of substantial deformation errors; here, standard analysis procedures significantly under-perform and fail to identify the true signal.

SUBMITTER: Kim WH

PROVIDER: S-EPMC4816646 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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On Statistical Analysis of Neuroimages with Imperfect Registration.

Kim Won Hwa WH Ravi Sathya N SN Johnson Sterling C SC Okonkwo Ozioma C OC Singh Vikas V

Proceedings. IEEE International Conference on Computer Vision 20151201

A variety of studies in neuroscience/neuroimaging seek to perform statistical inference on the acquired brain image scans for diagnosis as well as understanding the pathological manifestation of diseases. To do so, an important first step is to register (or co-register) all of the image data into a common coordinate system. This permits meaningful comparison of the intensities at each voxel across groups (e.g., diseased versus healthy) to evaluate the effects of the disease and/or use machine le ...[more]

PMID: 27042168

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Project description:1. In recent years, there has been a fast development of models that adjust for imperfect detection. These models have revolutionized the analysis of field data, and their use has repeatedly demonstrated the importance of sampling design and data quality. There are, however, several practical limitations associated with the use of detectability models which restrict their relevance to tropical conservation science. 2. We outline the main advantages of detectability models, before examining their limitations associated with their applicability to the analysis of tropical communities, rare species and large-scale data sets. Finally, we discuss whether detection probability needs to be controlled before and/or after data collection. 3. Models that adjust for imperfect detection allow ecologists to assess data quality by estimating uncertainty and to obtain adjusted ecological estimates of populations and communities. Importantly, these models have allowed informed decisions to be made about the conservation and management of target species. 4. Data requirements for obtaining unadjusted estimates are substantially lower than for detectability-adjusted estimates, which require relatively high detection/recapture probabilities and a number of repeated surveys at each location. These requirements can be difficult to meet in large-scale environmental studies where high levels of spatial replication are needed, or in the tropics where communities are composed of many naturally rare species. However, while imperfect detection can only be adjusted statistically, covariates of detection probability can also be controlled through study design. Using three study cases where we controlled for covariates of detection probability through sampling design, we show that the variation in unadjusted ecological estimates from nearly 100 species was qualitatively the same as that obtained from adjusted estimates. Finally, we discuss that the decision as to whether one should control for covariates of detection probability through study design or statistical analyses should be dependent on study objectives. 5.Synthesis and applications. Models that adjust for imperfect detection are an important part of an ecologist's toolkit, but they should not be uniformly adopted in all studies. Ecologists should never let the constraints of models dictate which questions should be pursued or how the data should be analysed, and detectability models are no exception. We argue for pluralism in scientific methods, particularly where cost-effective applied ecological science is needed to inform conservation policy at a range of different scales and in many different systems.

Project description:In adaptive radiation therapy of prostate cancer, fast and accurate registration between the planning image and treatment images of the patient is of essential importance. With the authors' recently developed deformable surface model, prostate boundaries in each treatment image can be rapidly segmented and their correspondences (or relative deformations) to the prostate boundaries in the planning image are also established automatically. However, the dense correspondences on the nonboundary regions, which are important especially for transforming the treatment plan designed in the planning image space to each treatment image space, are remained unresolved. This paper presents a novel approach to learn the statistical correlation between deformations of prostate boundary and nonboundary regions, for rapidly estimating deformations of the nonboundary regions when given the deformations of the prostate boundary at a new treatment image.The main contributions of the proposed method lie in the following aspects. First, the statistical deformation correlation will be learned from both current patient and other training patients, and further updated adaptively during the radiotherapy. Specifically, in the initial treatment stage when the number of treatment images collected from the current patient is small, the statistical deformation correlation is mainly learned from other training patients. As more treatment images are collected from the current patient, the patient-specific information will play a more important role in learning patient-specific statistical deformation correlation to effectively reflect prostate deformation of the current patient during the treatment. Eventually, only the patient-specific statistical deformation correlation is used to estimate dense correspondences when a sufficient number of treatment images have been acquired from the current patient. Second, the statistical deformation correlation will be learned by using a multiple linear regression (MLR) model, i.e., ridge regression (RR) model, which has the best prediction accuracy than other MLR models such as canonical correlation analysis (CCA) and principal component regression (PCR).To demonstrate the performance of the proposed method, we first evaluate its registration accuracy by comparing the deformation field predicted by our method with the deformation field estimated by the thin plate spline (TPS) based correspondence interpolation method on 306 serial prostate CT images of 24 patients. The average predictive error on the voxels around 5 mm of prostate boundary is 0.38 mm for our method of RR-based correlation model. Also, the corresponding maximum error is 2.89 mm. We then compare the speed for deformation interpolation by different methods. When considering the larger region of interest (ROI) with the size of 512 × 512 × 61, our method takes 24.41 seconds to interpolate the dense deformation field while TPS method needs 6.7 minutes; when considering a small ROI (surrounding prostate) with size of 112 × 110 × 93, our method takes 1.80 seconds, while TPS method needs 25 seconds.Experimental results show that the proposed method can achieve much faster registration speed yet with comparable registration accuracy, compared to the TPS-based correspondence (or deformation) interpolation approach.

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Project description:OBJECTIVES:To analyse prospective versus retrospective trial registration trends on the Australian New Zealand Clinical Trials Registry (ANZCTR) and to evaluate the reasons for non-compliance with prospective registration. DESIGN:Part 1: Descriptive analysis of trial registration trends from 2006 to 2015. Part 2: Online registrant survey. PARTICIPANTS:Part 1: All interventional trials registered on ANZCTR from 2006 to 2015. Part 2: Random sample of those who had retrospectively registered a trial on ANZCTR between 2010 and 2015. MAIN OUTCOME MEASURES:Part 1: Proportion of prospective versus retrospective clinical trial registrations (ie, registration before versus after enrolment of the first participant) on the ANZCTR overall and by various key metrics, such as sponsor, funder, recruitment country and sample size. Part 2: Reasons for non-compliance with prospective registration and perceived usefulness of various proposed mechanisms to improve prospective registration compliance. RESULTS:Part 1: Analysis of the complete dataset of 9450 trials revealed that compliance with prospective registration increased from 48% (216 out of 446 trials) in 2006 to 63% (723/1148) in 2012 and has since plateaued at around 64%. Patterns of compliance were relatively consistent across sponsor and funder types (industry vs non-industry), type of intervention (drug vs non-drug) and size of trial (n<100, 100-500, >500). However, primary sponsors from Australia/New Zealand were almost twice as likely to register prospectively (62%; 4613/7452) compared with sponsors from other countries with a WHO Network Registry (35%; 377/1084) or sponsors from countries without a WHO Registry (29%; 230/781). Part 2: The majority (56%; 84/149) of survey respondents cited lack of awareness as a reason for not registering their study prospectively. Seventy-four per cent (111/149) stated that linking registration to ethics approval would facilitate prospective registration. CONCLUSIONS:Despite some progress, compliance with prospective registration remains suboptimal. Linking registration to ethics approval was the favoured strategy among those sampled for improving compliance.

Dataset Information

On Statistical Analysis of Neuroimages with Imperfect Registration.

Publications

On Statistical Analysis of Neuroimages with Imperfect Registration.

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