Development and field assessment of a quantitative PCR for the detection and enumeration of the noxious bloom-former Anabaena planktonica

Anabaena planktonica is a harmful, bloom-forming freshwater cyanobacterium, which has arrived recently in New Zealand. In the short time since its incursion (<10 yr), A. planktonica has spread rapidly throughout lakes in the North Island. To date, the identification and enumeration of A. planktonica has been undertaken using light microscopy. There is an urgent demand for a highly sensitive and specific quantitative detection method that can be combined with a high sample processing capability in order to increase sampling frequency. In this study, we sequenced 36 cyanobacterial 16S rRNA genes (partial), complete intergenic transcribed spacers (ITS), and 23S rRNA genes (partial) of fresh-water cyanobacteria found in New Zealand. The sequences were used to develop an A. Planktonica specific TaqMan QPCR assay targeting the long ITS1-L and the 5´ terminus of the 23S rRNA gene. The QPCR method was linear (R2 = 0.999) over seven orders of magnitude with a lower end sensitivity of approximately five A. planktonica cells in the presence of exogenous DNA. The quantitative PCR (QPCR) method was used to assess the spatial distribution and seasonal population dynamics of A. planktonica from the Lower Karori Reservoir (Wellington, New Zealand) over a five-month period. The QPCR results were compared directly to microscopic cell counts and found to correlate significantly (95% confidence level) under both bloom and non-bloom conditions. The current QPCR assay will be an invaluable tool for routine monitoring programs and in research investigating environmental factors that regulate the population dynamics and the blooming of A. planktonica

Deformable Registration through Learning of Context-Specific Metric Aggregation

We propose a novel weakly supervised discriminative algorithm for learning context specific registration metrics as a linear combination of conventional similarity measures. Conventional metrics have been extensively used over the past two decades and therefore both their strengths and limitations are known. The challenge is to find the optimal relative weighting (or parameters) of different metrics forming the similarity measure of the registration algorithm. Hand-tuning these parameters would result in sub optimal solutions and quickly become infeasible as the number of metrics increases. Furthermore, such hand-crafted combination can only happen at global scale (entire volume) and therefore will not be able to account for the different tissue properties. We propose a learning algorithm for estimating these parameters locally, conditioned to the data semantic classes. The objective function of our formulation is a special case of non-convex function, difference of convex function, which we optimize using the concave convex procedure. As a proof of concept, we show the impact of our approach on three challenging datasets for different anatomical structures and modalities.Comment: Accepted for publication in the 8th International Workshop on Machine Learning in Medical Imaging (MLMI 2017), in conjunction with MICCAI 201

Hindcasting cyanobacterial communities in Lake Okaro with germination experiments and genetic analyses

Cyanobacterial blooms are becoming increasingly prevalent worldwide. Sparse historic phytoplankton records often result in uncertainty as to whether bloom-forming species have always been present and are proliferating in response to eutrophication or climate change, or if there has been a succession of new arrivals through recent history. This study evaluated the relative efficacies of germination experiments and automated rRNA intergenic spacer analysis (ARISA) assays in identifying cyanobacteria in a sediment core and thus reconstructing the historical composition of cyanobacterial communities. A core (360 mm in depth) was taken in the central, undisturbed basin of Lake Okaro, New Zealand, a lake with a rapid advance of eutrophication and increasing cyanobacteria populations. The core incorporated a tephra from an 1886 volcanic eruption that served to delineate recent sediment deposition. ARISA and germination experiments successfully detected akinete-forming nostocaleans in sediment dating 120 bp and showed little change in Nostocales species structure over this time scale. Species that had not previously been documented in the lake were identified including Aphanizomenon issatschenkoi, a potent anatoxin-a producer. The historic composition of Chrococcales and Oscillatoriales was more difficult to reconstruct, potentially due to the relatively rapid degradation of vegetative cells within sediment

Concurrent ischemic lesion age estimation and segmentation of CT brain using a transformer-based network

The cornerstone of stroke care is expedient management that varies depending on the time since stroke onset. Consequently, clinical decision making is centered on accurate knowledge of timing and often requires a radiologist to interpret Computed Tomography (CT) of the brain to confirm the occurrence and age of an event. These tasks are particularly challenging due to the subtle expression of acute ischemic lesions and the dynamic nature of their appearance. Automation efforts have not yet applied deep learning to estimate lesion age and treated these two tasks independently, so, have overlooked their inherent complementary relationship. To leverage this, we propose a novel end-to-end multi-task transformer-based network optimized for concurrent segmentation and age estimation of cerebral ischemic lesions. By utilizing gated positional self-attention and CT-specific data augmentation, the proposed method can capture long-range spatial dependencies while maintaining its ability to be trained from scratch under low-data regimes commonly found in medical imaging. Furthermore, to better combine multiple predictions, we incorporate uncertainty by utilizing quantile loss to facilitate estimating a probability density function of lesion age. The effectiveness of our model is then extensively evaluated on a clinical dataset consisting of 776 CT images from two medical centers. Experimental results demonstrate that our method obtains promising performance, with an area under the curve (AUC) of 0.933 for classifying lesion ages ≤4.5 hours compared to 0.858 using a conventional approach, and outperforms task-specific state-of-the-art algorithms

Large-scale Quality Control of Cardiac Imaging in Population Studies: Application to UK Biobank

In large population studies such as the UK Biobank (UKBB), quality control of the acquired images by visual assessment is unfeasible. In this paper, we apply a recently developed fully-automated quality control pipeline for cardiac MR (CMR) images to the first 19,265 short-axis (SA) cine stacks from the UKBB. We present the results for the three estimated quality metrics (heart coverage, inter-slice motion and image contrast in the cardiac region) as well as their potential associations with factors including acquisition details and subject-related phenotypes. Up to 14.2% of the analysed SA stacks had sub-optimal coverage (i.e. missing basal and/or apical slices), however most of them were limited to the first year of acquisition. Up to 16% of the stacks were affected by noticeable inter-slice motion (i.e. average inter-slice misalignment greater than 3.4 mm). Inter-slice motion was positively correlated with weight and body surface area. Only 2.1% of the stacks had an average end-diastolic cardiac image contrast below 30% of the dynamic range. These findings will be highly valuable for both the scientists involved in UKBB CMR acquisition and for the ones who use the dataset for research purposes

Bridging the Gap: Differentially Private Equivariant Deep Learning for Medical Image Analysis

Machine learning with formal privacy-preserving techniques like Differential Privacy (DP) allows one to derive valuable insights from sensitive medical imaging data while promising to protect patient privacy, but it usually comes at a sharp privacy-utility trade-off. In this work, we propose to use steerable equivariant convolutional networks for medical image analysis with DP. Their improved feature quality and parameter efficiency yield remarkable accuracy gains, narrowing the privacy-utility gap.Comment: Accepted as extended abstract at GeoMedIA Workshop 2022 (https://openreview.net/forum?id=rGYfMrMxI17

Learning under Distributed Weak Supervision

The availability of training data for supervision is a frequently encountered bottleneck of medical image analysis methods. While typically established by a clinical expert rater, the increase in acquired imaging data renders traditional pixel-wise segmentations less feasible. In this paper, we examine the use of a crowdsourcing platform for the distribution of super-pixel weak annotation tasks and collect such annotations from a crowd of non-expert raters. The crowd annotations are subsequently used for training a fully convolutional neural network to address the problem of fetal brain segmentation in T2-weighted MR images. Using this approach we report encouraging results compared to highly targeted, fully supervised methods and potentially address a frequent problem impeding image analysis research

Model-free Probabilistic Movement Primitives for physical interaction

Physical interaction in robotics is a complex problem that requires not only accurate reproduction of the kinematic trajectories but also of the forces and torques exhibited during the movement. We base our approach on Movement Primitives (MP), as MPs provide a framework for modelling complex movements and introduce useful operations on the movements, such as generalization to novel situations, time scaling, and others. Usually, MPs are trained with imitation learning, where an expert demonstrates the trajectories. However, MPs used in physical interaction either require additional learning approaches, e.g., reinforcement learning, or are based on handcrafted solutions. Our goal is to learn and generate movements for physical interaction that are learned with imitation learning, from a small set of demonstrated trajectories. The Probabilistic Movement Primitives (ProMPs) framework is a recent MP approach that introduces beneficial properties, such as combination and blending of MPs, and represents the correlations present in the movement. The ProMPs provides a variable stiffness controller that reproduces the movement but it requires a dynamics model of the system. Learning such a model is not a trivial task, and, therefore, we introduce the model-free ProMPs, that are learning jointly the movement and the necessary actions from a few demonstrations. We derive a variable stiffness controller analytically. We further extent the ProMPs to include force and torque signals, necessary for physical interaction. We evaluate our approach in simulated and real robot tasks

Prior-based Coregistration and Cosegmentation

We propose a modular and scalable framework for dense coregistration and cosegmentation with two key characteristics: first, we substitute ground truth data with the semantic map output of a classifier; second, we combine this output with population deformable registration to improve both alignment and segmentation. Our approach deforms all volumes towards consensus, taking into account image similarities and label consistency. Our pipeline can incorporate any classifier and similarity metric. Results on two datasets, containing annotations of challenging brain structures, demonstrate the potential of our method.Comment: The first two authors contributed equall