检索结果-内蒙古大学图书馆

A Marker-Less Registration Approach for Mixed Reality-Aided Maxillofacial Surgery: a Pilot Evaluation

JOURNAL OF DIGITAL IMAGING 2019年第6期32卷 1008-1018页

作者： Pepe, Antonio Trotta, Gianpaolo Francesco Mohr-Ziak, Peter Gsaxner, Christina Wallner, Juergen Bevilacqua, Vitoantonio Egger, Jan Graz Univ Technol Fac Comp Sci & Biomed Engn Inst Comp Graph & Vis A-8010 Graz Austria Comp Algorithms Med Lab Graz Austria Polytech Univ Bari Dept Mech Math & Management Via Orabona 4 Bari Italy VRVis Zentrum Virtual Real & Visualisierung Forsc Donau City Str 11 A-1220 Vienna Austria Med Univ Graz Dept Oral & Maxillofacial Surg Auenbruggerpl 5-1 A-8036 Graz Styria Austria Polytech Univ Bari Dept Elect & Informat Engn Via Orabona 4 Bari Italy

As of common routine in tumor resections, surgeons rely on local examinations of the removed tissues and on the swiftly made microscopy findings of the pathologist, which are based on intraoperatively taken tissue probes. This approach may imply an extended duration of the operation, increased effort for the medical staff, and longer occupancy of the operating room (OR). Mixed reality technologies, and particularly augmented reality, have already been applied in surgical scenarios with positive initial outcomes. Nonetheless, these methods have used manual or marker-based registration. In this work, we design an application for a marker-less registration of PET-CT information for a patient. The algorithm combines facial landmarks extracted from an RGB video stream, and the so-called Spatial-Mapping API provided by the HMD Microsoft HoloLens. The accuracy of the system is compared with a marker-based approach, and the opinions of field specialists have been collected during a demonstration. A survey based on the standard ISO-9241/110 has been designed for this purpose. The measurements show an average positioning error along the three axes of (x, y, z) = (3.3 +/- 2.3, - 4.5 +/- 2.9, - 9.3 +/- 6.1) mm. compared with the marker-based approach, this shows an increment of the positioning error of approx. 3 mm along two dimensions (x, y), which might be due to the absence of explicit markers. The application has been positively evaluated by the specialists;they have shown interest in continued further work and contributed to the development process with constructive criticism.

关键词： computer Assisted Surgery Microsoft HoloLens Pattern Recognition Head and Neck Cancer Augmented Reality Visualization Facial Landmarks Registration

来源：评论

学校读者我要写书评

暂无评论

Multi-Threaded Integration of HTC-Vive and MeVislab

Multi-Threaded Integration of HTC-Vive and MeVisLab

引用

Conference on medical Imaging - Imaging Informatics for Healthcare, Research, and Applications

作者： Gunacker, Simon Gall, Markus Schmalstieg, Dieter Egger, Jan Graz Univ Technol Inst Comp Graph & Vis Inffeldgasse 16c-2 A-8010 Graz Austria Comp Algorithms Med Cafe Lab A-8010 Graz Styria Austria AIT Austrian Inst Technol Dept Safety & Secur Donau City Str 1 A-1220 Vienna Austria BioTechMed Graz Krenngasse 37-1 A-8010 Graz Austria

ISBN: (数字)9781510616486

ISBN: (纸本)9781510616486

This work presents how Virtual Reality (VR) can easily be integrated into medical applications via a plugin for a medical image processing framework called MeVislab. A multi-threaded plugin has been developed using OpenVR, a VR library that can be used for developing vendor and platform independent VR applications. The plugin is tested using the HTC Vive, a head-mounted display developed by HTC and Valve Corporation.

关键词： Virtual Reality HTC Vive Valve Corporation Threads MeVislab Module

来源：评论

学校读者我要写书评

暂无评论

Genetic algorithm optimized feature transformation - A comparison with different classifiers

引用

5th Annual Genetic and Evolutionary computation Conference (GECCO 2003)

作者： Huang, ZJ Pei, M Goodman, E Huang, Y Li, GP Michigan State Univ Genet Algorithms Res & Applicat Grp E Lansing MI 48824 USA E China Normal Univ Ctr Comp Shanghai 200062 Peoples R China Fudan Univ Coll Med Electrocardiog Res Lab Shanghai Peoples R China

ISBN: (纸本)3540406034

When using a Genetic Algorithm (GA) to optimize the feature space of pattern classification problems, the performance improvement is not only determined by the data set used, but also depends on the classifier. This work compares the improvements achieved by GA-optimized feature transformations on several simple classifiers. Some traditional feature transformation techniques, such as Principal components Analysis (PCA) and Linear Discriminant Analysis (LDA) are also tested to see their effects on the GA optimization. The results based on some real-world data and five benchmark data sets from the UCI repository show that the improvements after GA-optimized feature transformation are in reverse ratio with the original classification rate if the classifier is used alone. It is also shown that performing the PCA and LDA transformations on the feature space prior to the GA optimization improved the final result.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Methods for Precision Dosing in Anticancer Drug Therapy: A Scoping Review

引用

CLINICAL PHARMACOKINETICS 2024年第9期63卷 1221-1237页

作者： Teplytska, Olga Ernst, Moritz Koltermann, Luca Marie Valderrama, Diego Trunz, Elena Vaisband, Marc Hasenauer, Jan Froehlich, Holger Jaehde, Ulrich Univ Bonn Inst Pharm Dept Clin Pharm Immenburg 4 D-53121 Bonn Germany Univ Cologne Fac Med & Univ Hosp Cologne Inst Publ Hlth Cologne Germany Fraunhofer Inst Algorithms & Sci Comp SCAI Dept Bioinformat St Augustin Germany Univ Bonn Inst Comp Sci 2 Visual Comp Bonn Germany Univ Bonn Hausdorff Ctr Math Bonn Germany Univ Bonn Inst Life & Med Sci LIMES Bonn Germany Paracelsus Med Univ Salzburg Lab Immunol & Mol Canc Res SCRI LIMCR Salzburg Canc Res Inst Dept Internal Med 3Haematol Med Oncol Haemostase Salzburg Austria Univ Bonn Bonn Aachen Int Ctr Informat Technol B IT Bonn Germany

IntroductionIn the last decade, various Machine Learning techniques have been proposed aiming to individualise the dose of anticancer drugs mostly based on a presumed drug effect or measured effect biomarkers. The aim of this scoping review was to comprehensively summarise the research status on the use of Machine Learning for precision dosing in anticancer drug *** scoping review was conducted in accordance with the interim guidance by Cochrane and the Joanna Briggs Institute. We systematically searched the databases medline (via Pubmed), Embase and the Cochrane Library for research articles and reviews including results published after 2016. Results were reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) *** total of 17 relevant studies was identified. In 12 of the included studies, Reinforcement Learning methods were used, including Classical, Deep, Double Deep and Conservative Q-Learning and Fuzzy Reinforcement Learning. Furthermore, classical Machine Learning methods were compared in terms of their performance and an artificial intelligence platform based on parabolic equations was used to guide dosing prospectively and retrospectively, albeit only in a limited number of patients. Due to the significantly different algorithm structures, a meaningful comparison between the various Machine Learning approaches was not ***, this review emphasises the clinical relevance of Machine Learning methods for anticancer drug dose optimisation, as many algorithms have shown promising results enabling model-free predictions with the potential to maximise efficacy and minimise toxicity when compared to standard protocols.

关键词：

来源：评论

学校读者我要写书评

暂无评论

DockFlow - a prototypic PharmaGrid for Virtual Screening Integrating Four Different Docking Tools

DockFlow - a prototypic PharmaGrid for Virtual Screening Int...

引用

7th Healthgrid Conference

作者： Wolf, Antje Hofmann-Apitius, Martin Ghanem, Moustafa Azam, Nabeel Kalaitzopoulos, Dimitrios Yu, Kunqian Kasam, Vinod Fraunhofer Inst Algorithms & Sci Comp SCAI D-53754 St Augustin Germany InforSense Ltd London W6 7JP England Chinese Acad Sci Drug Discovery & Design Ctr Shanghai Inst Mat Med State Key Lab Drug Res Shanghai 201203 Peoples R China

ISBN: (纸本)9781607500278

In this paper we present DockFlow, a prototypic version of a PharmaGrid DockFlow is supporting pharmaceutical research through enabling virtual screening on the Grid The system was developed in the course of the BRIDGE project funded by the European Commission Grids have been used before to run compute- and data-intensive virtual screening experiments, like in the WISDOM project With DockFlow, however, we addressed a variety of problems yet unsolved, like the diversity of results produced by different docking tools We also addressed the problem of analysing the data produced in a distributed virtual screening system applying a combinatorial docking approach In DockFlow we worked on a grid-based problem solving environment for virtual screening with the following major features execution of four different docking services (FlexX, AutoDock, DOCK and GAsDock) at locations in Europe and China remotely from a common workflow, storage of the results in a common Docking Database providing a shared analysis platform for the collaboration partners and combination of the results The DockFlow prototype is evaluated on two scientific case studies malaria and avian flu

关键词： Virtual Screening Molecular Docking FlexX AutoDock DOCK GAsDock Workflows Docking Database Grid computing

来源：评论

学校读者我要写书评

暂无评论

Detection, segmentation, simulation and visualization of aortic dissections: A review

引用

medICAL IMAGE ANALYSIS 2020年 65卷 101773-101773页

作者： Pepe, Antonio Li, Jianning Rolf-Pissarczyk, Malte Gsaxner, Christina Chen, Xiaojun Holzapfel, Gerhard A. Egger, Jan Graz Univ Technol Inst Comp Graph & Vis Inffeldgasse 16 A-8010 Graz Austria Stanford Univ Sch Med Dept Radiol 291 Campus Dr Stanford CA 94305 USA Comp Algorithms Med Lab Graz Austria BioTechMed Graz Mozartgasse 12-2 A-8010 Graz Austria Graz Univ Technol Inst Biomech Stremayrgasse 16-2 A-8010 Graz Austria Med Univ Graz Dept Oral & Maxillofacial Surg Auenbruggerpl 2 A-8036 Graz Austria Shanghai Jiao Tong Univ Sch Mech Engn 800 Dong Chuan Rd Shanghai 200240 Peoples R China Norwegian Univ Sci & Technol NTNU Dept Struct Engn N-7491 Trondheim Norway

Aortic dissection (AD) is a condition of the main artery of the human body, resulting in the formation of a new flow channel, or false lumen. The disease is usually diagnosed with a computed tomography angiography scan during the acute phase. A better understanding of the causes of AD requires knowledge of the aortic geometry (segmentation), including the true and false lumina, which is very time-consuming to reconstruct when performed manually on a slice-by-slice basis. Hence, different automatic and semiautomatic medical image analysis approaches have been proposed for this task over the last years. In this review, we present and discuss these computing techniques used to segment dissected aortas, also in regard to the detection and visualization of clinically relevant information and features from dissected aortas for customized patient-specific treatments. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Aorta Dissection Detection Segmentation Visualization Simulation computed tomography

来源：评论

学校读者我要写书评

暂无评论

Automated cross-sectional view selection in CT angiography of aortic dissections with uncertainty awareness and retrospective clinical annotations

引用

compUTERS IN BIOLOGY AND medICINE 2023年第1期165卷 107365-107365页

作者： Pepe, Antonio Egger, Jan Codari, Marina Willemink, Martin J. Gsaxner, Christina Li, Jianning Roth, Peter M. Schmalstieg, Dieter Mistelbauer, Gabriel Fleischmann, Dominik Graz Univ Technol Inst Comp Graph & Vis Inffeldgasse 16-2 A-8010 Graz Austria Stanford Univ Sch Med 3D & Quantitat Imaging Lab 300 Pasteur Dr Stanford CA 94305 USA Comp Algorithms Med Cafe Lab Graz Austria Univ Med Essen Inst AI Med IKIM Girardetstr 2 D-45131 Essen Germany

Surveillance imaging of patients with chronic aortic diseases, such as aneurysms and dissections, relies on obtaining and comparing cross-sectional diameter measurements along the aorta at predefined aortic landmarks, over time. The orientation of the cross-sectional measuring planes at each landmark is currently defined manually by highly trained operators. Centerline-based approaches are unreliable in patients with chronic aortic dissection, because of the asymmetric flow channels, differences in contrast opacification, and presence of mural thrombus, making centerline computations or measurements difficult to generate and reproduce. In this work, we present three alternative approaches - INS, MCDS, MCDbS - based on convolutional neural networks and uncertainty quantification methods to predict the orientation (phi, theta) of such cross-sectional planes. For the monitoring of chronic aortic dissections, we show how a dataset of 162 CTA volumes with overall 3273 imperfect manual annotations routinely collected in a clinic can be efficiently used to accomplish this task, despite the presence of non-negligible interoperator variabilities in terms of mean absolute error (MAE) and 95% limits of agreement (LOA). We show how, despite the large limits of agreement in the training data, the trained model provides faster and more reproducible results than either an expert user or a centerline method. The remaining disagreement lies within the variability produced by three independent expert annotators and matches the current state of the art, providing a similar error, but in a fraction of the time.

关键词： Imperfect annotations Aortic dissection Measurement Reproducibility Double-oblique reformation

来源：评论

学校读者我要写书评

暂无评论

Transfer learning with chest X-rays for ER patient classification

引用

SCIENTIFIC REPORTS 2020年第1期10卷 1-9页

作者： Stubblefield, Jonathan Hervert, Mitchell Causey, Jason L. Qualls, Jake A. Dong, Wei Cai, Lingrui Fowler, Jennifer Bellis, Emily Walker, Karl Moore, Jason H. Nehring, Sara Huang, Xiuzhen Arkansas State Univ Joint Translat Res Lab Ctr No Boundary Thinking CNBT St Bernards Med Ctr Jonesboro AR 72467 USA St Bernards Med Ctr Internal Med Residency Program 225 E Jackson Ave Jonesboro AR 72401 USA Arkansas State Univ Dept Comp Sci & Mol Biosci Program Jonesboro AR 72467 USA Ann Arbor Algorithms Ann Arbor MI 48103 USA Univ Penn Inst Biomed Informat Philadelphia PA 19104 USA Univ Arkansas Pine Bluff Dept Math & Comp Sci Pine Bluff AR 55455 USA Arkansas State Univ Arkansas Biosci Inst Jonesboro AR 72467 USA

One of the challenges with urgent evaluation of patients with acute respiratory distress syndrome (ARDS) in the emergency room (ER) is distinguishing between cardiac vs infectious etiologies for their pulmonary findings. We conducted a retrospective study with the collected data of 171 ER patients. ER patient classification for cardiac and infection causes was evaluated with clinical data and chest X-ray image data. We show that a deep-learning model trained with an external image data set can be used to extract image features and improve the classification accuracy of a data set that does not contain enough image data to train a deep-learning model. An analysis of clinical feature importance was performed to identify the most important clinical features for ER patient classification. The current model is publicly available with an interface at the web link: http://***/.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A comprehensive Workflow and Framework for immersive Virtual Endoscopy of dissected Aortae from CTA Data

A comprehensive Workflow and Framework for immersive Virtual...

引用

medical Imaging Conference - Image-Guided Procedures, Robotic Interventions, and Modeling

作者： Egger, Jan Gunacker, Simon Pepe, Antonio Melito, Gian Marco Gsaxner, Christina Li, Jianning Ellermann, Katrin Chen, Xiaojun Graz Univ Technol Inst Comp Graph & Vis Inffeldgasse 16c-2 A-8010 Graz Austria Med Univ Graz Dept Oral & Maxillofacial Surg Auenbruggerpl 5-1 A-8036 Graz Austria Shanghai Jiao Tong Univ Sch Mech Engn 800 Dong Chuan Rd Shanghai 200240 Peoples R China Comp Algorithms Med Lab A-8010 Graz Austria Stanford Univ Sch Med Dept Radiol 300 Pasteur Dr Stanford CA USA Graz Univ Technol Inst Mech Kopernikusgasse 24 A-8010 Graz Austria

ISBN: (纸本)9781510633988

The human organism is a highly complex system that is prone to various diseases. Some diseases are more dangerous than others, especially those that affect the circulatory system or the aorta in particular. The aorta is the largest artery in the human body. Its wall comprises several layers. When the intima, i.e. the innermost layer of the aortic wall, tears, blood enters and propagates between the layers causing them to separate. This is known as aortic dissection (AD). Without immediate treatment, an AD may kill 33% of patients within the first 24 hours, 50% of patients within 48 hours, and 75% of patients within 2 weeks. However, proper treatment is still subject to research and active discussion. By providing a deeper understanding of aortic dissections, this work aims to contribute to the continuous improvement of AD diagnosis and treatment by presenting AD in a new, immersive visual experience: Virtual Reality (VR). The visualization is based on computed Tomography (CT) scans of human patients suffering from an AD. Given a scan, relevant visual information is segmented, refined and put into a 3D scene. Further enhanced by blood flow simulation and VR user interaction, the visualization helps in better understanding AD. The current implementation serves as a prototype and is considered to be extended by minimizing user interaction when new CT scans are loaded into VR (i) and by providing an interface to feed the visualization with simulation data provided by mathematical models (ii).

关键词： Aortic Dissections Virtual Reality HTC Vive Valve Corporation Unity computed Tomography

来源：评论

学校读者我要写书评

暂无评论

Neuroimaging Feature Terminology: A Controlled Terminology for the Annotation of Brain Imaging Features

引用

JOURNAL OF ALZHEIMERS DISEASE 2017年第4期59卷 1153-1169页

作者： Iyappan, Anandhi Younesi, Erfan Redolfi, Alberto Vrooman, Henri Khanna, Shashank Frisoni, Giovanni B. Hofmann-Apitius, Martin Fraunhofer Inst Algorithms & Sci Comp SCAI Dept Bioinformat D-53754 St Augustin Germany Friedrich Wilhelms Univ Bonn Bonn Aachen Int Ctr Informat Technol Rhein Friedrich Wilhelms Univ Bonn Bonn Germany IRCCS San Giovanni Dio Fatebenefratelli Lab Epidemiol & Neuroimaging Brescia Italy Erasmus MC Univ Med Ctr Dept Radiol Biomed Imaging Grp Rotterdam Rotterdam Netherlands Erasmus MC Univ Med Ctr Dept Med Informat Biomed Imaging Grp Rotterdam Rotterdam Netherlands Univ Hosp Memory Clin Geneva Switzerland Univ Hosp Lab Neuroimagerie Vieillissement LANVIE Geneva Switzerland Univ Geneva Geneva Switzerland

Ontologies and terminologies are used for interoperability of knowledge and data in a standard manner among interdisciplinary research groups. Existing imaging ontologies capture general aspects of the imaging domain as a whole such as methodological concepts or calibrations of imaging instruments. However, none of the existing ontologies covers the diagnostic features measured by imaging technologies in the context of neurodegenerative diseases. Therefore, the Neuro-Imaging Feature Terminology (NIFT) was developed to organize the knowledge domain of measured brain features in association with neurodegenerative diseases by imaging technologies. The purpose is to identify quantitative imaging biomarkers that can be extracted from multi-modal brain imaging data. This terminology attempts to cover measured features and parameters in brain scans relevant to disease progression. In this paper, we demonstrate the systematic retrieval of measured indices from literature and how the extracted knowledge can be further used for disease modeling that integrates neuroimaging features with molecular processes.

关键词： Alzheimer's disease annotation brain neuroimaging terminology

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：