Probabilistic segmentation propagation from uncertainty in registration

In this paper we propose a novel approach for incorporating measures of spatial uncertainty which are derived from non-rigid registration, into propagated segmentation labels. In current approaches to segmentation via label propagation, a point-estimate of the registration parameters is used. However, this is limited by the registration accuracy achieved. In this work, we derive local measurements of the uncertainty of a non-rigid mapping from a probabilistic registration framework. This allows us to consider the set of probable locations for a segmentation label to hold. We demonstrate the use of this method on the propagation of accurately delineated cortical labels in inter-subject brain MRI using the NIREP dataset. We find that accounting for the spatial uncertainty of the mapping increases the sensitivity of correctly classifying anatomical labels

A Probabilistic Approach To Non-Rigid Medical Image Registration

Non-rigid image registration is an important tool for analysing morphometric differences in subjects with Alzheimer's disease from structural magnetic resonance images of the brain. This thesis describes a novel probabilistic approach to non-rigid registration of medical images, and explores the benefits of its use in this area of neuroimaging. Many image registration approaches have been developed for neuroimaging. The vast majority suffer from two limitations: Firstly, the trade-off between image fidelity and regularisation requires selection. Secondly, only a point-estimate of the mapping between images is inferred, overlooking the presence of uncertainty in the estimation. This thesis introduces a novel probabilistic non-rigid registration model and inference scheme. This framework allows the inference of the parameters that control the level of regularisation, and data fidelity in a data-driven fashion. To allow greater flexibility, this model is extended to allow the level of data fidelity to vary across space. A benefit of this approach, is that the registration can adapt to anatomical variability and other image acquisition differences. A further advantage of the proposed registration framework is that it provides an estimate of the distribution of probable transformations. Additional novel contributions of this thesis include two proposals for exploiting the estimated registration uncertainty. The first of these estimates a local image smoothing filter, which is based on the registration uncertainty. The second approach incorporates the distribution of transformations into an ensemble learning scheme for statistical prediction. These techniques are integrated into standard frameworks for morphometric analysis, and are demonstrated to improve the ability to distinguish subjects with Alzheimer's disease from healthy controls

Fast Predictive Image Registration

We present a method to predict image deformations based on patch-wise image appearance. Specifically, we design a patch-based deep encoder-decoder network which learns the pixel/voxel-wise mapping between image appearance and registration parameters. Our approach can predict general deformation parameterizations, however, we focus on the large deformation diffeomorphic metric mapping (LDDMM) registration model. By predicting the LDDMM momentum-parameterization we retain the desirable theoretical properties of LDDMM, while reducing computation time by orders of magnitude: combined with patch pruning, we achieve a 1500x/66x speed up compared to GPU-based optimization for 2D/3D image registration. Our approach has better prediction accuracy than predicting deformation or velocity fields and results in diffeomorphic transformations. Additionally, we create a Bayesian probabilistic version of our network, which allows evaluation of deformation field uncertainty through Monte Carlo sampling using dropout at test time. We show that deformation uncertainty highlights areas of ambiguous deformations. We test our method on the OASIS brain image dataset in 2D and 3D

Longitudinal Image Registration with Temporal-order and Subject-specificity Discrimination

Morphological analysis of longitudinal MR images plays a key role in monitoring disease progression for prostate cancer patients, who are placed under an active surveillance program. In this paper, we describe a learning-based image registration algorithm to quantify changes on regions of interest between a pair of images from the same patient, acquired at two different time points. Combining intensity-based similarity and gland segmentation as weak supervision, the population-data-trained registration networks significantly lowered the target registration errors (TREs) on holdout patient data, compared with those before registration and those from an iterative registration algorithm. Furthermore, this work provides a quantitative analysis on several longitudinal-data-sampling strategies and, in turn, we propose a novel regularisation method based on maximum mean discrepancy, between differently-sampled training image pairs. Based on 216 3D MR images from 86 patients, we report a mean TRE of 5.6 mm and show statistically significant differences between the different training data sampling strategies.Comment: Accepted at MICCAI 202

Diffeomorphic brain shape modelling using Gauss-Newton optimisation

Shape modelling describes methods aimed at capturing the natural variability of shapes and commonly relies on probabilistic interpretations of dimensionality reduction techniques such as principal component analysis. Due to their computational complexity when dealing with dense deformation models such as diffeomorphisms, previous attempts have focused on explicitly reducing their dimension, diminishing de facto their flexibility and ability to model complex shapes such as brains. In this paper, we present a generative model of shape that allows the covariance structure of deformations to be captured without squashing their domain, resulting in better normalisation. An efficient inference scheme based on Gauss-Newton optimisation is used, which enables processing of 3D neuroimaging data. We trained this algorithm on segmented brains from the OASIS database, generating physiologically meaningful deformation trajectories. To prove the model’s robustness, we applied it to unseen data, which resulted in equivalent fitting scores

Investigating the Host-Range of the Rust Fungus Puccinia psidii sensu lato across Tribes of the Family Myrtaceae Present in Australia

The exotic rust fungus Puccinia psidii sensu lato was first detected in Australia in April 2010. This study aimed to determine the host-range potential of this accession of the rust by testing its pathogenicity on plants of 122 taxa, representative of the 15 tribes of the subfamily Myrtoideae in the family Myrtaceae. Each taxon was tested in two separate trials (unless indicated otherwise) that comprised up to five replicates per taxon and six replicates of a positive control (Syzygium jambos). No visible symptoms were observed on the following four taxa in either trial: Eucalyptus grandis×camaldulensis, E. moluccana, Lophostemon confertus and Sannantha angusta. Only small chlorotic or necrotic flecks without any uredinia (rust fruiting bodies) were observed on inoculated leaves of seven other taxa (Acca sellowiana, Corymbia calophylla ‘Rosea’, Lophostemon suaveolens, Psidium cattleyanum, P. guajava ‘Hawaiian’ and ‘Indian’, Syzygium unipunctatum). Fully-developed uredinia were observed on all replicates across both trials of 28 taxa from 8 tribes belonging to the following 17 genera: Agonis, Austromyrtus, Beaufortia, Callistemon, Calothamnus, Chamelaucium, Darwinia, Eucalyptus, Gossia, Kunzea, Leptospermum, Melaleuca, Metrosideros, Syzygium, Thryptomene, Tristania, Verticordia. In contrast, the remaining 83 taxa inoculated, including the majority of Corymbia and Eucalyptus species, developed a broad range of symptoms, often across the full spectrum, from fully-developed uredinia to no visible symptoms. These results were encouraging as they indicate that some levels of genetic resistance to the rust possibly exist in these taxa. Overall, our results indicated no apparent association between the presence or absence of disease symptoms and the phylogenetic relatedness of taxa. It is most likely that the majority of the thousands of Myrtaceae species found in Australia have the potential to become infected to some degree by the rust, although this wide host range may not be fully realized in the field

Longitudinal brain MRI analysis with uncertain registration

In this paper we propose a novel approach for incorporating measures of spatial uncertainty, which are derived from non-rigid registration, into spatially normalised statistics. Current approaches to spatially normalised statistical analysis use point-estimates of the registration parameters. This is limiting as the registration will rarely be completely accurate, and therefore data smoothing is often used to compensate for the uncertainty of the mapping. We derive localised measurements of spatial uncertainty from a probabilistic registration framework, which provides a principled approach to image smoothing. We evaluate our method using longitudinal deformation features from a set of MR brain images acquired from the Alzheimer's Disease Neuroimaging Initiative. These images are spatially normalised using our probabilistic registration algorithm. The spatially normalised longitudinal features are adaptively smoothed according to the registration uncertainty. The proposed adaptive smoothing shows improved classification results, (84% correct Alzheimer's Disease vs. controls), over either not smoothing (79.6%), or using a Gaussian filter with σ = 2mm (78.8%). © 2011 Springer-Verlag

A Bayesian approach for spatially adaptive regularisation in non-rigid registration

This paper introduces a novel method for inferring spatially varying regularisation in non-rigid registration. This is achieved through full Bayesian inference on a probabilistic registration model, where the prior on transformations is parametrised as a weighted mixture of spatially localised components. Such an approach has the advantage of allowing the registration to be more flexibly driven by the data than a more traditional global regularisation scheme, such as bending energy. The proposed method adaptively determines the influence of the prior in a local region. The importance of the prior may be reduced in areas where the data better supports deformations, or can enforce a stronger constraint in less informative areas. Consequently, the use of such a spatially adaptive prior may reduce the unwanted impact of regularisation on the inferred deformation field. This is especially important for applications such as tensor based morphometry, where the features of interest are directly derived from the deformation field. The proposed approach is demonstrated with application to tensor based morphometry analysis of subjects with Alzheimer's disease and healthy controls. The results show that using the proposed spatially adaptive prior leads to deformation fields that have a substantially lower average complexity, but which also provide more accurate localisation of statistical group differences. © 2013 Springer-Verlag

Study of the selectivity of alpha(1)-adrenergic antagonists by molecular modeling of alpha(1a)-, alpha(1b)-, and alpha(1d)-adrenergic receptor subtypes and docking simulations

Modeling of alpha(1a), alpha(1b), and alpha(1d) adrenergic receptor subtypes has been performed using InsightII software and bovine rhodopsin as a template. Adrenaline and noradrenaline, as endogenous agonists, were docked to validate the developed models, explore the putative binding sites, and calculate relative docking scores. alpha(1)-Adrenergic antagonists with the highest order of selectivity and activity at specific receptor subtypes were then chosen for docking into the corresponding receptor models. Docking simulations were performed using the FlexX module implemented in the Sybil program. PMF scoring functions of the obtained complexes calculated as relative to PMF scoring functions for noradrenaline-receptor subtype complexes were then used for correlation with selectivity on different alpha(1)-adrenergic subtypes. Good correlations were obtained for most receptor subtype-selectivity pairs: (1) using PMF scores calculated for ligands in complex with alpha(1a)-receptor subtype, r = 0.7503 for alpha(1a/1b) and r = 0.6336 for alpha(1a/1d) selectivity; (2) using PMF scores calculated for ligands in complex with alpha(1b) receptor subtype, r = 0.7632 for alpha(1a/1b) and r = 0.7061 for alpha(1b/1d) selectivity; (3) using PMF scores for ligands in complex with alpha(1d) receptor subtype, r = 0.7377 for alpha(1a/1d) and r = 0.9913 for alpha(1b/1d) selectivity.Peer reviewe