Understanding the marine environment : seabed habitat investigations of the Dogger Bank offshore draft SAC

This report details work carried out by the Centre for Environment, Fisheries and Aquaculture Science (Cefas), British Geological Surveys (BGS) and Envision Ltd. for the Joint Nature Conservation Committee (JNCC). It has been produced to provide the JNCC with evidence on the distribution and extent of Annex I habitat (including variations of these features) on the Dogger Bank in advance of its possible designation as a Special Area of Conservation (SAC). The report contains information required under Regulation 7 of the Conservation (Natural Habitats, &c.) Regulations 2007 and will enable the JNCC to advise the Department for Environment, Food and Rural Affairs (Defra) as to whether the site is deemed eligible as a SAC. The report provides detailed information about the Dogger Bank and evaluates its features of interest according to the Habitats Directive selection criteria and guiding principles. This assessment has been made following a thorough analysis of existing information combined with newly acquired field survey data collected using ‘state of the art’ equipment. In support of this process acoustic (sidescan sonar and multibeam echosounder) and groundtruthing data (Hamon grabs, trawls and underwater video) were collected during a 19-day cruise on RV Cefas Endeavour, which took place between 2-20 April 2008. Existing information and newly acquired data were combined to investigate the sub-surface geology, surface sediments and bedforms, epifaunal and infaunal communities of the Dogger Bank. Results were integrated into a habitat map employing the EUNIS classification. Key results are as follows: • The upper Pleistocene Dogger Bank Formation dictates the shape of the Dogger Bank. • The Dogger Bank is morphologically distinguishable from the surrounding seafloor following the application of a technique, which differentiates the degree of slope. • A sheet of Holocene sediments of variable thickness overlies the Dogger Bank Formation. At the seabed surface, these Holocene sediments can be broadly delineated into fine sands and coarse sediments. • Epifaunal and infaunal communities were distinguished based on multivariate analysis of data derived from video and stills analysis and Hamon grab samples. Sediment properties and depth were the main factors controlling the distribution of infauna and epifauna across the Bank. • Epifaunal and infaunal community links were explored. Most stations could be categorised according to one of four combined infaunal/epifaunal community types (i.e. sandy sediment bank community, shallow sandy sediment bank community, coarse sediment bank community or deep community north of the bank). • Biological zones were identified using modelling techniques based on light climate and wave base data. Three biological zones, namely infralittoral, circalittoral and deep circalittoral are present in the study site. • EUNIS level 4 habitats were mapped by integrating acoustic, biological, physical and optical data. Eight different habitats are present on the Dogger Bank. This report also provides some of the necessary information and data to help the JNCC ultimately reach a judgement as to whether the Dogger Bank is suitable as an SAC. In support of this process the encountered habitats and the ecology of the Dogger Bank are compared with other SACs known to contain sandbank habitats in UK waters. The functional and ecological importance of the Dogger Bank as well as potential anthropogenic impacts is discussed. A scientific justification underlying the proposed Dogger Bank dSAC boundary is also given (Appendix 1). This is followed by a discussion of the suitability and cost-effectiveness of techniques utilised for seabed investigations of the Dogger Bank. Finally, recommendations for strategies and techniques employed for investigation of Annex I sandbanks are provided

Content–based fMRI Brain Maps Retrieval

The statistical analysis of functional magnetic resonance imaging (fMRI) is used to extract functional data of cerebral activation during a given experimental task. It allows for assessing changes in cerebral function related to cerebral activities. This methodology has been widely used and a few initiatives aim to develop shared data resources. Searching these data resources for a specific research goal remains a challenging problem. In particular, work is needed to create a global content–based (CB) fMRI retrieval capability. This work presents a CB fMRI retrieval approach based on the brain activation maps extracted using Probabilistic Independent Component Analysis (PICA). We obtained promising results on data from a variety of experiments which highlight the potential of the system as a tool that provides support for finding hidden similarities between brain activation maps

Graph Representation for Content–based fMRI Activation Map Retrieval

The use of functional magnetic resonance imaging (fMRI) to visualize brain activity in a non–invasive way is an emerging technique in neuroscience. It is expected that data sharing and the development of better search tools for the large amount of existing fMRI data may lead to a better understanding of the brain through the use of larger sample sizes or allowing collaboration among experts in various areas of expertise. In fact, there is a trend toward such sharing of fMRI data, but there is a lack of tools to effectively search fMRI data repositories, a factor which limits further research use of these repositories. Content–based (CB) fMRI brain map retrieval tools may alleviate this problem. A CB–fMRI brain map retrieval tool queries a brain activation map collection (containing brain maps showing activation areas after a stimulus is applied to a subject), and retrieves relevant brain activation maps, i.e. maps that are similar to the query brain activation map. In this work, we propose a graph–based representation for brain activation maps with the goal of improving retrieval accuracy as compared to existing methods. In this brain graph, nodes represent different specialized regions of a functional–based brain atlas. We evaluated our approach using human subject data obtained from eight experiments where a variety of stimuli were applied

Vertebra Shape Classification using MLP for Content-Based Image Retrieval

A desirable content-based image retrieval (CBIR) system would classify extracted image features to support some form of semantic retrieval. The Lister Hill National Center for Biomedical Communications, an intramural R&D division of the National Library for Medicine (NLM), maintains an archive of digitized X-rays of the cervical and lumbar spine taken as part of the second national health and nutrition examination survey (NHANES II). It is our goal to provide shape-based access to digitized X-rays including retrieval on automatically detected and classified pathology, e.g., anterior osteophytes. This is done using radius of curvature analysis along the anterior portion, and morphological analysis for quantifying protrusion regions along the vertebra boundary. Experimental results are presented for the classification of 704 cervical spine vertebrae by evaluating the features using a multi-layer perceptron (MLP) based approach. In this paper, we describe the design and current status of the content-based image retrieval (CBIR) system and the role of neural networks in the design of an effective multimedia information retrieval system

Synthetic Sample Selection via Reinforcement Learning

Synthesizing realistic medical images provides a feasible solution to the shortage of training data in deep learning based medical image recognition systems. However, the quality control of synthetic images for data augmentation purposes is under-investigated, and some of the generated images are not realistic and may contain misleading features that distort data distribution when mixed with real images. Thus, the effectiveness of those synthetic images in medical image recognition systems cannot be guaranteed when they are being added randomly without quality assurance. In this work, we propose a reinforcement learning (RL) based synthetic sample selection method that learns to choose synthetic images containing reliable and informative features. A transformer based controller is trained via proximal policy optimization (PPO) using the validation classification accuracy as the reward. The selected images are mixed with the original training data for improved training of image recognition systems. To validate our method, we take the pathology image recognition as an example and conduct extensive experiments on two histopathology image datasets. In experiments on a cervical dataset and a lymph node dataset, the image classification performance is improved by 8.1% and 2.3%, respectively, when utilizing high-quality synthetic images selected by our RL framework. Our proposed synthetic sample selection method is general and has great potential to boost the performance of various medical image recognition systems given limited annotation.Comment: MICCAI202

Live Wire Segmentation Tool for Osteophyte Detection in Lumbar Spine X-Ray Images

Computer-assisted vertebra segmentation in x-ray images is a challenging problem. Inter-subject variability and the generally poor contrast of digitized radiograph images contribute to the segmentation difficulty. In this paper, a semi-automated live wire approach is investigated for vertebrae segmentation. The live wire approach integrates initially selected user points with dynamic programming to generate a closed vertebra boundary. In order to assess the degree to which vertebra features are conserved using the live wire technique, convex hull-based features to characterize anterior osteophytes in lumbar vertebrae are determined for live wire and manually segmented vertebrae. Anterior osteophyte discrimination was performed over 405 lumbar vertebrae, 204 abnormal vertebrae with anterior osteophytes and 201 normal vertebrae. A leave-one-out standard back propagation neural network was used for vertebrae segmentation. Experimental results show that manual segmentation yielded slightly better discrimination results than the live wire technique

Image Analysis Techniques for the Automated Evaluation of Subaxial Subluxation in Cervical Spine X-Ray Images

Rheumatoid arthritis is a chronic inflammatory disease affecting synovial joints of the body, especially the hands and feet, spine, knees and hips. For many patients, the cervical spine is associated with rheumatoid arthritis. Subluxation is the abnormal movement of one of the bones that comprise a joint. In this research, image analysis techniques have been investigated for the recognition of cervical spine x-ray images with one or more instances of subaxial subluxation. Receiver operating characteristic curve results are presented, showing potential for subaxial subluxation discrimination on an image-by-image basis

Growing North American indigenous corn

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