1,863 research outputs found

    Optimierte Bildgebung in der nuklearmedizinischen Diagnostik – Patient*innen im Mittelpunkt

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    Die molekulare Bildgebung umfasst unterschiedliche Technologien und Methoden und sie unterliegt einem kontinuierlichen Fortschritt, wobei ihr ursprüngliches Herzstück – die „patient centered care“ – nicht vergessen werden darf. In der vorliegenden Habilitationsschrift konnte nachgewiesen werden, dass die Innovationen in der Gerätetechnik das Potenzial haben, die Patient*innenzufriedenheit signifikant zu verbessern, ohne dabei die Aussagekraft der Untersuchung einzuschränken. Die Translation zwischen verschiedenen Akquisitionstechniken ermöglichen neue Perspektiven im Gebiet der Radionomics sowie eine personalisierte Behandlung der Patienten*innen. Zudem können Optimierungen von Behandlungsprotokollen die untersuchungsbedingte Belastung für Patient*innen signifikant senken und sowohl der Patient*innensicherheit als auch einer zielgerichteten Ressourcenallokation gerecht werden können. Somit sollte sich die molekulare Bildgebung nicht auf Bildbetrachtung oder Bildinterpretation reduzieren lassen. Vielmehr müssen die Patient*innenzufriedenheit sowie -sicherheit, die Optimierung von Untersuchungsprotokollen, die Definition von Aufnahme- sowie Rekonstruktionsparametern und Ressourcenallokation weiter verbessert werden; um dieses in den richtigen Kontext einer optimalen Therapieentscheidung einzuordnen

    A cost focused framework for optimizing collection and annotation of ultrasound datasets

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    Machine learning for medical ultrasound imaging encounters a major challenge: the prohibitive costs of producing and annotating clinical data. The issue of cost vs size is well understood in the context of clinical trials. These same methods can be applied to optimize the data collection and annotation process, ultimately reducing machine learning project cost and times in feasibility studies. This paper presents a two-phase framework for quantifying the cost of data collection using iterative accuracy/sample size predictions and active learning to guide/optimize full human annotation in medical ultrasound imaging for machine learning purposes. The paper demonstrated potential cost reductions using public breast, fetal, and lung ultrasound datasets and a practical case study on Breast Ultrasound. The results show that just as with clinical trials, the relationship between dataset size and final accuracy can be predicted, with the majority of accuracy improvements occurring using only 40-50% of the data dependent on tolerance measure. Manual annotation can be reduced further using active learning, resulting in a representative cost reduction of 66% with a tolerance measure of around 4% accuracy drop from theoretical maximums. The significance of this work lies in its ability to quantify how much additional data and annotation will be required to achieve a specific research objective. These methods are already well understood by clinical funders and so provide a valuable and effective framework for feasibility and pilot studies where machine learning will be applied within a fixed budget to maximize predictive gains, informing resourcing and further clinical study

    High-Fidelity Low-Cost Synthetic Training Model for Fetoscopic Spina Bifida Repair

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    BACKGROUND: Fetoscopic Spina Bifida repair (fSB-repair) is increasingly being practiced, but limited skill acquisition poses a barrier to widespread adoption. Extensive training in relevant models, including both ex- and in-vivo models may help. To address this, a synthetic training model that is affordable, realistic and allows skill analysis would be useful.OBJECTIVE: To create a high-fidelity model for training the essential neurosurgical steps of fetoscopic spina bifida repair using synthetic materials. Additionally, we aimed to obtain a cheap and easily reproducible model.STUDY DESIGN: We developed a three-layered silicon-based model resembling the anatomical layers of a typical myelomeningocele lesion. It allows for filling the cyst with fluid and conducting a water tightness test post-repair. A compliant silicon ball mimics the uterine cavity, and is fixed to a solid 3D printed base. The fetal back with the lesion (single-use) is placed inside the uterine ball, which is reusable and repairable to allow practicing port insertion and fixation multiple times. Following cannula insertion, the uterus is insufflated, and clinical fetoscopic, robotic or prototype instruments can be used. Three skilled endoscopic surgeons each did six simulated fetoscopic repairs following the surgical steps of an open repair. The primary outcome was surgical success, based on water tightness of the repair, operation time &lt;180 minutes and an Objective-Structured-Assessment-of-Technical-Skills (OSATS)-score of ≥ 18/25. Skill retention was measured using a competence commulative sum (C-CUSUM) analysis on composite binary outcome for surgical success. Secondary outcomes were cost and fabrication time of the model.RESULTS: We made a model for simulating spina bifida repair neurosurgical steps with anatomical details, port insertion, placode release and descent, undermining of skin and muscular layer, and endoscopic suturing. The model is made with reusable 3D-printed molds with easily accessible materials. The one-time startup cost was 211€, and each single-use simulated MMC-lesion costs 9.5€ in materials and 50 min working hours. Two skilled endoscopic surgeons performed six simulated three-port fetoscopic repairs, while a third used a Da-Vinci surgical robot. Operation times decreased over 30% from the first to last trial. Six experiments per surgeon did not show an obvious OSATS-score improvement. C-CUSUM analysis confirmed competency for each surgeon.CONCLUSION: This high-fidelity low-cost spina bifida model allows simulated dissection and closure of a myelomeningocele lesion.</p

    Neural Architecture Search for Image Segmentation and Classification

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    Deep learning (DL) is a class of machine learning algorithms that relies on deep neural networks (DNNs) for computations. Unlike traditional machine learning algorithms, DL can learn from raw data directly and effectively. Hence, DL has been successfully applied to tackle many real-world problems. When applying DL to a given problem, the primary task is designing the optimum DNN. This task relies heavily on human expertise, is time-consuming, and requires many trial-and-error experiments. This thesis aims to automate the laborious task of designing the optimum DNN by exploring the neural architecture search (NAS) approach. Here, we propose two new NAS algorithms for two real-world problems: pedestrian lane detection for assistive navigation and hyperspectral image segmentation for biosecurity scanning. Additionally, we also introduce a new dataset-agnostic predictor of neural network performance, which can be used to speed-up NAS algorithms that require the evaluation of candidate DNNs

    La traduzione specializzata all’opera per una piccola impresa in espansione: la mia esperienza di internazionalizzazione in cinese di Bioretics© S.r.l.

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    Global markets are currently immersed in two all-encompassing and unstoppable processes: internationalization and globalization. While the former pushes companies to look beyond the borders of their country of origin to forge relationships with foreign trading partners, the latter fosters the standardization in all countries, by reducing spatiotemporal distances and breaking down geographical, political, economic and socio-cultural barriers. In recent decades, another domain has appeared to propel these unifying drives: Artificial Intelligence, together with its high technologies aiming to implement human cognitive abilities in machinery. The “Language Toolkit – Le lingue straniere al servizio dell’internazionalizzazione dell’impresa” project, promoted by the Department of Interpreting and Translation (Forlì Campus) in collaboration with the Romagna Chamber of Commerce (Forlì-Cesena and Rimini), seeks to help Italian SMEs make their way into the global market. It is precisely within this project that this dissertation has been conceived. Indeed, its purpose is to present the translation and localization project from English into Chinese of a series of texts produced by Bioretics© S.r.l.: an investor deck, the company website and part of the installation and use manual of the Aliquis© framework software, its flagship product. This dissertation is structured as follows: Chapter 1 presents the project and the company in detail; Chapter 2 outlines the internationalization and globalization processes and the Artificial Intelligence market both in Italy and in China; Chapter 3 provides the theoretical foundations for every aspect related to Specialized Translation, including website localization; Chapter 4 describes the resources and tools used to perform the translations; Chapter 5 proposes an analysis of the source texts; Chapter 6 is a commentary on translation strategies and choices

    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

    Application of radiomics in diagnosis and treatment of lung cancer

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    Radiomics has become a research field that involves the process of converting standard nursing images into quantitative image data, which can be combined with other data sources and subsequently analyzed using traditional biostatistics or artificial intelligence (Al) methods. Due to the capture of biological and pathophysiological information by radiomics features, these quantitative radiomics features have been proven to provide fast and accurate non-invasive biomarkers for lung cancer risk prediction, diagnosis, prognosis, treatment response monitoring, and tumor biology. In this review, radiomics has been emphasized and discussed in lung cancer research, including advantages, challenges, and drawbacks

    Image-based Decision Support Systems: Technical Concepts, Design Knowledge, and Applications for Sustainability

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    Unstructured data accounts for 80-90% of all data generated, with image data contributing its largest portion. In recent years, the field of computer vision, fueled by deep learning techniques, has made significant advances in exploiting this data to generate value. However, often computer vision models are not sufficient for value creation. In these cases, image-based decision support systems (IB-DSSs), i.e., decision support systems that rely on images and computer vision, can be used to create value by combining human and artificial intelligence. Despite its potential, there is only little work on IB-DSSs so far. In this thesis, we develop technical foundations and design knowledge for IBDSSs and demonstrate the possible positive effect of IB-DSSs on environmental sustainability. The theoretical contributions of this work are based on and evaluated in a series of artifacts in practical use cases: First, we use technical experiments to demonstrate the feasibility of innovative approaches to exploit images for IBDSSs. We show the feasibility of deep-learning-based computer vision and identify future research opportunities based on one of our practical use cases. Building on this, we develop and evaluate a novel approach for combining human and artificial intelligence for value creation from image data. Second, we develop design knowledge that can serve as a blueprint for future IB-DSSs. We perform two design science research studies to formulate generalizable principles for purposeful design — one for IB-DSSs and one for the subclass of image-mining-based decision support systems (IM-DSSs). While IB-DSSs can provide decision support based on single images, IM-DSSs are suitable when large amounts of image data are available and required for decision-making. Third, we demonstrate the viability of applying IBDSSs to enhance environmental sustainability by performing life cycle assessments for two practical use cases — one in which the IB-DSS enables a prolonged product lifetime and one in which the IB-DSS facilitates an improvement of manufacturing processes. We hope this thesis will contribute to expand the use and effectiveness of imagebased decision support systems in practice and will provide directions for future research

    MIS-FM: 3D Medical Image Segmentation using Foundation Models Pretrained on a Large-Scale Unannotated Dataset

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    Pretraining with large-scale 3D volumes has a potential for improving the segmentation performance on a target medical image dataset where the training images and annotations are limited. Due to the high cost of acquiring pixel-level segmentation annotations on the large-scale pretraining dataset, pretraining with unannotated images is highly desirable. In this work, we propose a novel self-supervised learning strategy named Volume Fusion (VF) for pretraining 3D segmentation models. It fuses several random patches from a foreground sub-volume to a background sub-volume based on a predefined set of discrete fusion coefficients, and forces the model to predict the fusion coefficient of each voxel, which is formulated as a self-supervised segmentation task without manual annotations. Additionally, we propose a novel network architecture based on parallel convolution and transformer blocks that is suitable to be transferred to different downstream segmentation tasks with various scales of organs and lesions. The proposed model was pretrained with 110k unannotated 3D CT volumes, and experiments with different downstream segmentation targets including head and neck organs, thoracic/abdominal organs showed that our pretrained model largely outperformed training from scratch and several state-of-the-art self-supervised training methods and segmentation models. The code and pretrained model are available at https://github.com/openmedlab/MIS-FM.Comment: 13 pages, 8 figure
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