Meta-Registration: Learning Test-Time Optimization for Single-Pair Image Registration

Neural networks have been proposed for medical image registration by learning, with a substantial amount of training data, the optimal transformations between image pairs. These trained networks can further be optimized on a single pair of test images - known as test-time optimization. This work formulates image registration as a meta-learning algorithm. Such networks can be trained by aligning the training image pairs while simultaneously improving test-time optimization efficacy; tasks which were previously considered two independent training and optimization processes. The proposed meta-registration is hypothesized to maximize the efficiency and effectiveness of the test-time optimization in the "outer" meta-optimization of the networks. For image guidance applications that often are time-critical yet limited in training data, the potentially gained speed and accuracy are compared with classical registration algorithms, registration networks without meta-learning, and single-pair optimization without test-time optimization data. Experiments are presented in this paper using clinical transrectal ultrasound image data from 108 prostate cancer patients. These experiments demonstrate the effectiveness of a meta-registration protocol, which yields significantly improved performance relative to existing learning-based methods. Furthermore, the meta-registration achieves comparable results to classical iterative methods in a fraction of the time, owing to its rapid test-time optimization process.Comment: Accepted to ASMUS 2022 Workshop at MICCA

Learning Generalized Non-Rigid Multimodal Biomedical Image Registration from Generic Point Set Data

Free Point Transformer (FPT) has been proposed as a data-driven, non-rigid point set registration approach using deep neural networks. As FPT does not assume constraints based on point vicinity or correspondence, it may be trained simply and in a flexible manner by minimizing an unsupervised loss based on the Chamfer Distance. This makes FPT amenable to real-world medical imaging applications where ground-truth deformations may be infeasible to obtain, or in scenarios where only a varying degree of completeness in the point sets to be aligned is available. To test the limit of the correspondence finding ability of FPT and its dependency on training data sets, this work explores the generalizability of the FPT from well-curated non-medical data sets to medical imaging data sets. First, we train FPT on the ModelNet40 dataset to demonstrate its effectiveness and the superior registration performance of FPT over iterative and learning-based point set registration methods. Second, we demonstrate superior performance in rigid and non-rigid registration and robustness to missing data. Last, we highlight the interesting generalizability of the ModelNet-trained FPT by registering reconstructed freehand ultrasound scans of the spine and generic spine models without additional training, whereby the average difference to the ground truth curvatures is 1.3 degrees, across 13 patients.Comment: Accepted to ASMUS 2022 Workshop at MICCA

A direct image of the obscuring disk surrounding an active galactic nucleus

Active galactic nuclei (AGN) are generally accepted to be powered by the release of gravitational energy in a compact accretion disk surrounding a massive black hole. Such disks are also necessary to collimate powerful radio jets seen in some AGN. The unifying classification schemes for AGN further propose that differences in their appearance can be attributed to the opacity of the accreting material, which may obstruct our view of the central region of some systems. The popular model for the obscuring medium is a parsec-scale disk of dense molecular gas, although evidence for such disks has been mostly indirect, as their angular size is much smaller than the resolution of conventional telescopes. Here we report the first direct images of a pc-scale disk of ionised gas within the nucleus of NGC 1068, the archetype of obscured AGN. The disk is viewed nearly edge-on, and individual clouds within the ionised disk are opaque to high-energy radiation, consistent with the unifying classification scheme. In projection, the disk and AGN axes align, from which we infer that the ionised gas disk traces the outer regions of the long-sought inner accretion disk.Comment: 14 pages, LaTeX, PSfig, to appear in Nature. also available at http://hethp.mpe-garching.mpg.de/Preprint

Rapid Lung Ultrasound COVID-19 Severity Scoring with Resource-Efficient Deep Feature Extraction

Artificial intelligence-based analysis of lung ultrasound imaging has been demonstrated as an effective technique for rapid diagnostic decision support throughout the COVID-19 pandemic. However, such techniques can require days- or weeks-long training processes and hyper-parameter tuning to develop intelligent deep learning image analysis models. This work focuses on leveraging 'off-the-shelf' pre-trained models as deep feature extractors for scoring disease severity with minimal training time. We propose using pre-trained initializations of existing methods ahead of simple and compact neural networks to reduce reliance on computational capacity. This reduction of computational capacity is of critical importance in time-limited or resource-constrained circumstances, such as the early stages of a pandemic. On a dataset of 49 patients, comprising over 20,000 images, we demonstrate that the use of existing methods as feature extractors results in the effective classification of COVID-19-related pneumonia severity while requiring only minutes of training time. Our methods can achieve an accuracy of over 0.93 on a 4-level severity score scale and provides comparable per-patient region and global scores compared to expert annotated ground truths. These results demonstrate the capability for rapid deployment and use of such minimally-adapted methods for progress monitoring, patient stratification and management in clinical practice for COVID-19 patients, and potentially in other respiratory diseases.Comment: Accepted to ASMUS 2022 Workshop at MICCA

Future possibilities in the prevention of breast cancer: Luteinizing hormone-releasing hormone agonists

The cyclic production of estrogen and progesterone by the premenopausal ovary accounts for the steep rise in breast cancer risk in premenopausal women. These hormones are breast cell mitogens. By reducing exposure to these ovarian hormones, agonists of luteinizing hormone-releasing hormone (LHRH) given to suppress ovarian function may prove useful in cancer prevention. To prevent deleterious effects of hypoestrogenemia, the addition of low-dose hormone replacement to the LHRH agonist appears necessary. Pilot data with such an approach indicates it is feasible and reduces mammographic densities

Image quality assessment for machine learning tasks using meta-reinforcement learning

In this paper, we consider image quality assessment (IQA) as a measure of how images are amenable with respect to a given downstream task, or task amenability. When the task is performed using machine learning algorithms, such as a neural-network-based task predictor for image classification or segmentation, the performance of the task predictor provides an objective estimate of task amenability. In this work, we use an IQA controller to predict the task amenability which, itself being parameterised by neural networks, can be trained simultaneously with the task predictor. We further develop a meta-reinforcement learning framework to improve the adaptability for both IQA controllers and task predictors, such that they can be fine-tuned efficiently on new datasets or meta-tasks. We demonstrate the efficacy of the proposed task-specific, adaptable IQA approach, using two clinical applications for ultrasound-guided prostate intervention and pneumonia detection on X-ray images

Investigating the health implications of social policy initiatives at the local level: study design and methods

<p>Abstract</p> <p>Background</p> <p>In this paper we present the research design and methods of a study that seeks to capture local level responses to an Australian national social policy initiative, aimed at reducing inequalities in the social determinants of health.</p> <p>Methods/Design</p> <p>The study takes a policy-to-practice approach and combines policy and stakeholder interviewing with a comparative case study analysis of two not-for-profit organisations involved in the delivery of federal government policy.</p> <p>Discussion</p> <p>Before the health impacts of broad-scale policies, such as the one described in this study, can be assessed at the population level, we need to understand the implementation process. This is consistent with current thinking in political science and social policy, which has emphasised the importance of investigating how, and if, policies are translated into operational realities.</p

Alcohol and HIV Disease Progression: Weighing the Evidence

Heavy alcohol use is commonplace among HIV-infected individuals; however, the extent that alcohol use adversely impacts HIV disease progression has not been fully elucidated. Fairly strong evidence suggests that heavy alcohol consumption results in behavioral and biological processes that likely increase HIV disease progression, and experimental evidence of the biological effect of heavy alcohol on simian immunodeficiency virus in macaques is quite suggestive. However, several observational studies of the effect of heavy alcohol consumption on HIV progression conducted in the 1990s found no association of heavy alcohol consumption with time to AIDS diagnosis, while some more recent studies showed associations of heavy alcohol consumption with declines of CD4 cell counts and nonsuppression of HIV viral load. We discuss several plausible biological and behavioral mechanisms by which alcohol may cause HIV disease progression, evidence from prospective observational human studies, and suggest future research to further illuminate this important issue

Is Ankyrin a genetic risk factor for psychiatric phenotypes?

Background Genome wide association studies reported two single nucleotide polymorphisms in ANK3 (rs9804190 and rs10994336) as independent genetic risk factors for bipolar disorder. Another SNP in ANK3 (rs10761482) was associated with schizophrenia in a large European sample. Within the debate on common susceptibility genes for schizophrenia and bipolar disorder, we tried to investigate common findings by analyzing association of ANK3 with schizophrenia, bipolar disorder and unipolar depression. Methods We genotyped three single nucleotide polymorphisms (SNPs) in ANK3 (rs9804190, rs10994336, and rs10761482) in a case-control sample of German descent including 920 patients with schizophrenia, 400 with bipolar affective disorder, 220 patients with unipolar depression according to ICD 10 and 480 healthy controls. Sample was further differentiated according to Leonhard's classification featuring disease entities with specific combination of bipolar and psychotic syndromes. Results We found no association of rs9804190 and rs10994336 with bipolar disorder, unipolar depression or schizophrenia. In contrast to previous findings rs10761482 was associated with bipolar disorder (p = 0.015) but not with schizophrenia or unipolar depression. We observed no association with disease entities according to Leonhard's classification. Conclusion Our results support a specific genetic contribution of ANK3 to bipolar disorder though we failed to replicate findings for schizophrenia. We cannot confirm ANK3 as a common risk factor for different diseases