925 research outputs found

    Automated Distinct Bone Segmentation from Computed Tomography Images using Deep Learning

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    Large-scale CT scans are frequently performed for forensic and diagnostic purposes, to plan and direct surgical procedures, and to track the development of bone-related diseases. This often involves radiologists who have to annotate bones manually or in a semi-automatic way, which is a time consuming task. Their annotation workload can be reduced by automated segmentation and detection of individual bones. This automation of distinct bone segmentation not only has the potential to accelerate current workflows but also opens up new possibilities for processing and presenting medical data for planning, navigation, and education. In this thesis, we explored the use of deep learning for automating the segmentation of all individual bones within an upper-body CT scan. To do so, we had to find a network architec- ture that provides a good trade-off between the problem’s high computational demands and the results’ accuracy. After finding a baseline method and having enlarged the dataset, we set out to eliminate the most prevalent types of error. To do so, we introduced an novel method called binary-prediction-enhanced multi-class (BEM) inference, separating the task into two: Distin- guishing bone from non-bone is conducted separately from identifying the individual bones. Both predictions are then merged, which leads to superior results. Another type of error is tack- led by our developed architecture, the Sneaky-Net, which receives additional inputs with larger fields of view but at a smaller resolution. We can thus sneak more extensive areas of the input into the network while keeping the growth of additional pixels in check. Overall, we present a deep-learning-based method that reliably segments most of the over one hundred distinct bones present in upper-body CT scans in an end-to-end trained matter quickly enough to be used in interactive software. Our algorithm has been included in our groups virtual reality medical image visualisation software SpectoVR with the plan to be used as one of the puzzle piece in surgical planning and navigation, as well as in the education of future doctors

    Under construction: infrastructure and modern fiction

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    In this dissertation, I argue that infrastructural development, with its technological promises but widening geographic disparities and social and environmental consequences, informs both the narrative content and aesthetic forms of modernist and contemporary Anglophone fiction. Despite its prevalent material forms—roads, rails, pipes, and wires—infrastructure poses particular formal and narrative problems, often receding into the background as mere setting. To address how literary fiction theorizes the experience of infrastructure requires reading “infrastructurally”: that is, paying attention to the seemingly mundane interactions between characters and their built environments. The writers central to this project—James Joyce, William Faulkner, Karen Tei Yamashita, and Mohsin Hamid—take up the representational challenges posed by infrastructure by bringing transit networks, sanitation systems, and electrical grids and the histories of their development and use into the foreground. These writers call attention to the political dimensions of built environments, revealing the ways infrastructures produce, reinforce, and perpetuate racial and socioeconomic fault lines. They also attempt to formalize the material relations of power inscribed by and within infrastructure; the novel itself becomes an imaginary counterpart to the technologies of infrastructure, a form that shapes and constrains what types of social action and affiliation are possible

    Bridging Micro- and Macro- Evolution In Tropical Fishes

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    In marine environments, barriers to dispersal can be challenging to identify because they are often cryptic. Unlike terrestrial environments, where a mountain chain that is visible can physically separate two populations of animals, vast masses of water in the ocean make it challenging to pinpoint these barriers. Therefore, the impact of these barriers on the formation of new species in the ocean is still not well understood. While most marine populations have long been considered to be well connected via long-distance dispersal, molecular ecology studies are increasingly unveiling inconspicuous barriers that promote population divergence and ultimately speciation. The advent of genomic techniques that allow the generation of data for thousands of genes has provided an unprecedented opportunity to uncover marine barriers that were previously invisible using more rudimentary tools. This, in turn, has opened new avenues for understanding of how barriers to dispersal affect population connectivity in the marine environment. The overarching goal of my dissertation is to use genome-wide data to look for genetic patterns that correspond to such barriers, and to test for their effect at short-, intermediate- and long-term evolutionary scales, going through a continuum from micro- to macro-evolution, in a time span from thousands to millions of years. At the short-scale, I examined two controversial cases of species delimitation. Species delimitation is a major question in biology and is essential for adequate management of organismal diversity. The first challenging case involves the red snappers in the Western Atlantic. Red snappers have been traditionally recognized as two separate species based on morphology: Lutjanus campechanus (northern red snapper) and L. purpureus (southern red snapper). However, recent genetic studies using few molecular markers failed to delineate these nominal species, lumping the northern and southern populations into a single species (L. campechanus). To evaluate if the populations of these fish represent one or two species, my project applied ca. 40,000 genome-wide markers of 178 individuals collected throughout the range of the two species and population and species delimitation analyses. Overall, my results supported the isolation and differentiation of these species, a result that confirmed the morphology-based delimitation scenario, highlighting the benefits of using genome-wide data in complex cases of species delimitation (Chapter I, published in Proc. Roy. Soc. B in 2019). The second study case involves a species complex of silverside fishes (Chirostoma humboltianum group: Atherinidae) in the Central Mexico plateau. The humboltianum group represents a taxonomically-controversial species complex where previous morphological and molecular studies based on a few genes produced conflicting species delineation scenarios. I applied an integrative approach that considered multiple lines of evidence to investigate the species numbers and boundaries comprising this contentious group. I used ca. 33,000 molecular markers for 77 individuals representing the nine nominal species in the group, spanning their distribution range in the central Mexico plateau, in combination with morphologic and ecologic information. My findings are inconsistent with the morphospecies and ecological delimitation scenarios, identifying three to four species. This study provides an atypical example in which genome-wide analyses delineate fewer species than previously recognized on the basis of morphological data alone. It also highlights the influence of geologic history as a main driver of speciation in the group (Chapter II, published in BMC Eco. Evol. B in 2022). At the intermediate- scale, I evaluated the influence of historical (e.g., geophysical events) and contemporary barriers (e.g., habitat gaps) hindering genetic flow among populations by studying the spatio-temporal phylogenetic concordance of co-distributed lineages. For this study, I investigated the comparative phylogeography of labrisomid blennies in the genus Malacoctenus. I generated data for ca. 28K genome-wide markers that were sequenced from over 500 individuals collected from 38 locations, representing 23 (out of 25) species of Malacoctenus. With this dataset, I assessed the effect of recognized historical (e.g., the rising of the Isthmus of Panama) and contemporary barriers (e.g., sandy gaps) in the Tropical Eastern Pacific (TEP) and the Tropical Atlantic (TA) biogeographic realms. These blennies represent an ideal system to test the effect of such barriers as they are strongly associated with rocky habitats and coral reefs. Therefore, subtle habitat disruptions may lead to genetic isolation. At the micro-evolutionary scale, the observed population structure patterns identified the Sinaloan and Central American breaks as the major breaks in the TEP; and the Bahamas and Eastern Caribbean breaks as key barriers disrupting connectivity in the TA. All in all, the effect of these breaks varies across species, suggesting that species-specific traits (e.g., habitat preference), also greatly influence their dispersal capabilities. My study identified five instances where marine barriers promoted the diversification of independent evolutionary lineages that could potentially represent species complexes. Some of them supported by evidence of population differentiation from previous morphological analyses as well as by my geometric morphometric analyses. Major environmental variables driving population differentiation in the TEP are depth, temperature, chlorophyll altogether with spatial components, while in the TA suspended particle matter also influences diversification. At the long-term scale, my results suggest that depth is a primary driver of speciation in the TEP, leading to niche divergence between tide pool- and reef-associated clades. In contrast, in the TA, patterns of environmental association appeared more intricate, where depth, temperature, chlorophyll and physical features significantly contributing to speciation in this region. Finally, our time-calibrated analyses at macroevolutionary scales elucidated an Eastern Atlantic origin of the clade followed by an east-to-west dispersal. Although the historical break attributed to the rise of the Isthmus of Panama had a substantial influence on the evolutionary history of the genus, our analyses demonstrate that it did not triggered synchronous cladogenetic events. In summary, by using a combination of population genomics, comparative phylogeography, phylogenomics, seascape genomics, and geometric morphometric approaches, this study highlights major contemporary and historical barriers hindering population connectivity in the TEP and TA biogeographic regions, enhancing our understanding of the forces and processes generating new species in marine systems (Chapter III, to be submitted for publication). All in all, my thesis highlights that the use of genome-wide data provides unprecedented resolution to unveil patterns of genetic structure, commonly unraveling cryptic diversity, and the opportunity to address species delimitation problems. By uncovering the spatio-temporal genetic patterns of fishes along the evolutionary continuum, my dissertation provides novel insights into the evolutionary and biogeographic history of marine and freshwater Neotropical fishes. Overall, my dissertation not only helps to understand the evolutionary history of the species under study, but more generally, elucidate factors driving evolutionary process in the marine realm, ranging from population-level scales, to speciation, to higher level relationships among groups

    Evaluation of different segmentation-based approaches for skin disorders from dermoscopic images

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    Treballs Finals de Grau d'Enginyeria BiomĂšdica. Facultat de Medicina i CiĂšncies de la Salut. Universitat de Barcelona. Curs: 2022-2023. Tutor/Director: Sala Llonch, Roser, Mata Miquel, Christian, Munuera, JosepSkin disorders are the most common type of cancer in the world and the incident has been lately increasing over the past decades. Even with the most complex and advanced technologies, current image acquisition systems do not permit a reliable identification of the skin lesion by visual examination due to the challenging structure of the malignancy. This promotes the need for the implementation of automatic skin lesion segmentation methods in order to assist in physicians’ diagnostic when determining the lesion's region and to serve as a preliminary step for the classification of the skin lesion. Accurate and precise segmentation is crucial for a rigorous screening and monitoring of the disease's progression. For the purpose of the commented concern, the present project aims to accomplish a state-of-the-art review about the most predominant conventional segmentation models for skin lesion segmentation, alongside with a market analysis examination. With the rise of automatic segmentation tools, a wide number of algorithms are currently being used, but many are the drawbacks when employing them for dermatological disorders due to the high-level presence of artefacts in the image acquired. In light of the above, three segmentation techniques have been selected for the completion of the work: level set method, an algorithm combining GrabCut and k-means methods and an intensity automatic algorithm developed by Hospital Sant Joan de DĂ©u de Barcelona research group. In addition, a validation of their performance is conducted for a further implementation of them in clinical training. The proposals, together with the got outcomes, have been accomplished by means of a publicly available skin lesion image database

    GAC-MAC-SGA 2023 Sudbury Meeting: Abstracts, Volume 46

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    2023- The Twenty-seventh Annual Symposium of Student Scholars

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    The full program book from the Twenty-seventh Annual Symposium of Student Scholars, held on April 18-21, 2023. Includes abstracts from the presentations and posters.https://digitalcommons.kennesaw.edu/sssprograms/1027/thumbnail.jp

    Image based real-time ice load prediction tool for ship and offshore platform in managed ice field

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    The increased activities in arctic water warrant modelling of ice properties and ice-structure interaction forces to ensure safe operations of ships and offshore platforms. Several established analytical and numerical ice force estimation models can be found in the literature. Recently, researchers have been working on Machine Learning (ML) based, data-driven force predictors trained on experimental data and field measurement. Application of both traditional and ML-based image processing for extracting information from ice floe images has also been reported in recent literature; because extraction of ice features from real-time videos and images can significantly improve ice force prediction. However, there exists room for improvement in those studies. For example, accurate extraction of ice floe information is still challenging because of their complex and varied shapes, colour similarities and reflection of light on them. Besides, real ice floes are often found in groups with overlapped and/or connected boundaries, making detecting even more challenging due to weaker edges in such situations. The development of an efficient coupled model, which will extract information from the ice floe images and train a force predictor based on the extracted dataset, is still an open problem. This research presents two Hybrid force prediction models. Instead of using analytical or numerical approaches, the Hybrid models directly extract floe characteristics from the images and later train ML-based force predictors using those extracted floe parameters. The first model extracted ice features from images using traditional image processing techniques and then used SVM and FFNN to develop two separate force predictors. The improved ice image processing technique used here can extract useful ice properties from a closely connected, unevenly illuminated floe field with various floe sizes and shapes. The second model extracted ice features from images using RCNN and then trained two separate force predictors using SVM and FFNN, similar to the first model. The dataset for training SVM and FFNN force predictors involved variables extracted from the image (floe number, density, sizes, etc.) and variables taken from the experimental analysis results (ship speed, floe thickness, force etc.). The performance of both Hybrid models in terms of image segmentation and force prediction, are analyzed and compared to establish their validity and applicability. Nevertheless, there exists room for further development of the proposed Hybrid models. For example, extend the current models to include more data and investigate other machine learning and deep learning-based network architectures to predict the ice force directly from the image as an input
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