Extending the Foundational Model of Anatomy with Automatically Acquired Spatial Relations

Formal ontologies have made significant impact in bioscience over the last ten years. Among them, the Foundational Model of Anatomy Ontology (FMA) is the most comprehensive model for the spatio-structural representation of human anatomy. In the research project MEDICO we use the FMA as our main source of background knowledge about human anatomy. Our ultimate goals are to use spatial knowledge from the FMA (1) to improve automatic parsing algorithms for 3D volume data sets generated by Computed Tomography and Magnetic Resonance Imaging and (2) to generate semantic annotations using the concepts from the FMA to allow semantic search on medical image repositories. We argue that in this context more spatial relation instances are needed than those currently available in the FMA. In this publication we present a technique for the automatic inductive acquisition of spatial relation instances by generalizing from expert-annotated volume datasets

Trying to break new ground in aerial archaeology

Aerial reconnaissance continues to be a vital tool for landscape-oriented archaeological research. Although a variety of remote sensing platforms operate within the earth’s atmosphere, the majority of aerial archaeological information is still derived from oblique photographs collected during observer-directed reconnaissance flights, a prospection approach which has dominated archaeological aerial survey for the past century. The resulting highly biased imagery is generally catalogued in sub-optimal (spatial) databases, if at all, after which a small selection of images is orthorectified and interpreted. For decades, this has been the standard approach. Although many innovations, including digital cameras, inertial units, photogrammetry and computer vision algorithms, geographic(al) information systems and computing power have emerged, their potential has not yet been fully exploited in order to re-invent and highly optimise this crucial branch of landscape archaeology. The authors argue that a fundamental change is needed to transform the way aerial archaeologists approach data acquisition and image processing. By addressing the very core concepts of geographically biased aerial archaeological photographs and proposing new imaging technologies, data handling methods and processing procedures, this paper gives a personal opinion on how the methodological components of aerial archaeology, and specifically aerial archaeological photography, should evolve during the next decade if developing a more reliable record of our past is to be our central aim. In this paper, a possible practical solution is illustrated by outlining a turnkey aerial prospection system for total coverage survey together with a semi-automated back-end pipeline that takes care of photograph correction and image enhancement as well as the management and interpretative mapping of the resulting data products. In this way, the proposed system addresses one of many bias issues in archaeological research: the bias we impart to the visual record as a result of selective coverage. While the total coverage approach outlined here may not altogether eliminate survey bias, it can vastly increase the amount of useful information captured during a single reconnaissance flight while mitigating the discriminating effects of observer-based, on-the-fly target selection. Furthermore, the information contained in this paper should make it clear that with current technology it is feasible to do so. This can radically alter the basis for aerial prospection and move landscape archaeology forward, beyond the inherently biased patterns that are currently created by airborne archaeological prospection

Fine-scale mapping of vector habitats using very high resolution satellite imagery : a liver fluke case-study

The visualization of vector occurrence in space and time is an important aspect of studying vector-borne diseases. Detailed maps of possible vector habitats provide valuable information for the prediction of infection risk zones but are currently lacking for most parts of the world. Nonetheless, monitoring vector habitats from the finest scales up to farm level is of key importance to refine currently existing broad-scale infection risk models. Using Fasciola hepatica, a parasite liver fluke as a case in point, this study illustrates the potential of very high resolution (VHR) optical satellite imagery to efficiently and semi-automatically detect detailed vector habitats. A WorldView2 satellite image capable of <5m resolution was acquired in the spring of 2013 for the area around Bruges, Belgium, a region where dairy farms suffer from liver fluke infections transmitted by freshwater snails. The vector thrives in small water bodies (SWBs), such as ponds, ditches and other humid areas consisting of open water, aquatic vegetation and/or inundated grass. These water bodies can be as small as a few m(2) and are most often not present on existing land cover maps because of their small size. We present a classification procedure based on object-based image analysis (OBIA) that proved valuable to detect SWBs at a fine scale in an operational and semi-automated way. The classification results were compared to field and other reference data such as existing broad-scale maps and expert knowledge. Overall, the SWB detection accuracy reached up to 87%. The resulting fine-scale SWB map can be used as input for spatial distribution modelling of the liver fluke snail vector to enable development of improved infection risk mapping and management advice adapted to specific, local farm situations

Digital technologies for virtual recomposition : the case study of Serpotta stuccoes

The matter that lies beneath the smooth and shining surface of stuccoes of the Serpotta family, who used to work in Sicily from 1670 to 1730, has been thoroughly studied in previous papers, disclosing the deep, even if empirical, knowledge of materials science that guided the artists in creating their master- works. In this work the attention is focused on the solid perspective and on the scenographic sculpture by Giacomo Serpotta, who is acknowledged as the leading exponent of the School. The study deals with some particular works of the artist, the so-called "teatrini" (Toy Theater), made by him for the San Lorenzo Oratory in Palermo. On the basis of archive documents and previous analogical photogrammetric plotting, integrated with digital solutions and methodologies of computer- based technologies, the study investigates and interprets the geometric-formal genesis of the examined works of art, until the prototyping of the whole scenic apparatus.peer-reviewe

Image mining: trends and developments

[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining

Towards Automatic SAR-Optical Stereogrammetry over Urban Areas using Very High Resolution Imagery

In this paper we discuss the potential and challenges regarding SAR-optical stereogrammetry for urban areas, using very-high-resolution (VHR) remote sensing imagery. Since we do this mainly from a geometrical point of view, we first analyze the height reconstruction accuracy to be expected for different stereogrammetric configurations. Then, we propose a strategy for simultaneous tie point matching and 3D reconstruction, which exploits an epipolar-like search window constraint. To drive the matching and ensure some robustness, we combine different established handcrafted similarity measures. For the experiments, we use real test data acquired by the Worldview-2, TerraSAR-X and MEMPHIS sensors. Our results show that SAR-optical stereogrammetry using VHR imagery is generally feasible with 3D positioning accuracies in the meter-domain, although the matching of these strongly hetereogeneous multi-sensor data remains very challenging. Keywords: Synthetic Aperture Radar (SAR), optical images, remote sensing, data fusion, stereogrammetr

Spatiotemporal data as the foundation of an archaeological stratigraphy extraction and management system

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PadChest: A large chest x-ray image dataset with multi-label annotated reports

We present a labeled large-scale, high resolution chest x-ray dataset for the automated exploration of medical images along with their associated reports. This dataset includes more than 160,000 images obtained from 67,000 patients that were interpreted and reported by radiologists at Hospital San Juan Hospital (Spain) from 2009 to 2017, covering six different position views and additional information on image acquisition and patient demography. The reports were labeled with 174 different radiographic findings, 19 differential diagnoses and 104 anatomic locations organized as a hierarchical taxonomy and mapped onto standard Unified Medical Language System (UMLS) terminology. Of these reports, 27% were manually annotated by trained physicians and the remaining set was labeled using a supervised method based on a recurrent neural network with attention mechanisms. The labels generated were then validated in an independent test set achieving a 0.93 Micro-F1 score. To the best of our knowledge, this is one of the largest public chest x-ray database suitable for training supervised models concerning radiographs, and the first to contain radiographic reports in Spanish. The PadChest dataset can be downloaded from http://bimcv.cipf.es/bimcv-projects/padchest/

Implicit cognition is impaired and dissociable in a head-injured group with executive deficits

Implicit or non-conscious cognition is traditionally assumed to be robust to pathology but Gomez-Beldarrain et al (1999, 2002) recently showed deficits on a single implicit task after head injury. Laboratory research suggests that implicit processes dissociate. This study therefore examined implicit cognition in 20 head-injured patients and age- and I.Q.-matched controls using a battery of four implicit cognition tasks: a Serial Reaction Time task (SRT), mere exposure effect task, automatic stereotype activation and hidden co-variation detection. Patients were assessed on an extensive neuropsychological battery, and MRI scanned. Inclusion criteria included impairment on at least one measure of executive function. The patient group was impaired relative to the control group on all the implicit cognition tasks except automatic stereotype activation. Effect size analyses using the control mean and standard deviation for reference showed further dissociations across patients and across implicit tasks. Patients impaired on implicit tasks had more cognitive deficits overall than those unimpaired, and a larger Dysexecutive Self/Other discrepancy (DEX) score suggesting greater behavioural problems. Performance on the SRT task correlated with a composite measure of executive function. Head-injury thus produced heterogeneous impairments in the implicit acquisition of new information. Implicit activation of existing knowledge structures appeared intact. Impairments in implicit cognition and executive function may interact to produce dysfunctional behaviour after head-injury. Future comparisons of implicit and explicit cognition should use several measures of each function, to ensure that they measure the latent variable of interest