Evaluation of a new software prototype for frameless radiosurgery of arteriovenous malformations

Abstract Background In order to locate an arteriovenous malformation, typically, a digital subtraction angiography (DSA) is carried out. To use the DSA for target definition an accurate image registration between CT and DSA is required. Carrying out a non-invasive, frameless procedure, registration of the 2D-DSA images with the CT is critical. A new software prototype is enabling this frameless procedure. The aim of this work was to evaluate the prototype in terms of targeting accuracy and reliability based on phantom measurements as well as with the aid of patient data. In addition, the user’s ability to recognize registration mismatches and quality was assessed. Methods Targeting accuracy was measured with a simple cubic, as well as with an anthropomorphic head phantom. Clearly defined academic targets within the phantoms were contoured on the CT. These reference structures were compared with the structures generated within the prototype. A similar approach was used with patient data, where the clinically contoured target served as the reference structure. An important error source decreasing the target accuracy comes from registration errors between CT and 2D-DSA. For that reason, the tools in BC provided to the user to check these registrations are very important. In order to check if the user is able to recognize registration errors, a set of different registration errors was introduced to the correctly registered CT and 2D-DSA image data sets of three different patients. Each of six different users rated the whole set of registrations within the prototype. Results The target accuracy of the prototype was found to be below 0.04 cm for the cubic phantom and below 0.05 cm for the anthropomorphic head phantom. The mean target accuracy for the 15 patient cases was found to be below 0.3 cm. In the registration verification part, almost all introduced registration errors above 1° or 0.1 cm were detected by the six users. Nevertheless, in order to quantify and categorize the possibility to detect mismatches in the registration process more data needs to be evaluated. Conclusion Our study shows, that the prototype is a useful tool that has the potential to fill the gap towards a frameless procedure when treating AVMs with the aid of 2D-DSA images in radiosurgery. The target accuracy of the prototype is similar to other systems already established in clinical routine.

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Intelligent vehicle modeling design based on image processing

International Journal of Advanced Robotic Systems ◽

10.1177/1729881421993347 ◽

2021 ◽

Vol 18 (1) ◽

pp. 172988142199334

Author(s):

Guangchao Zhang ◽

Junrong Liu

Keyword(s):

Image Data ◽

Front Face ◽

Intelligent Vehicle ◽

Surface Model ◽

Data Sets ◽

Face Modeling ◽

Shape Coefficient ◽

Automotive Market ◽

Modeling Analysis ◽

Family Analysis

With the urgent demand of consumers for diversified automobile modeling, simple, efficient, and intelligent automobile modeling analysis and modeling method is an urgent problem to be solved in current automobile modeling design. The purpose of this article is to analyze the modeling preference and trend of the current automobile market in time, which can assist the modeling design of new models of automobile main engine factories and strengthen their branding family. Intelligent rapid modeling shortens the current modeling design cycle, so that the product rapid iteration is to occupy an active position in the automotive market. In this article, aiming at the family analysis of automobile front face, the image database of automobile front face modeling analysis was created. The database included two data sets of vehicle signs and no vehicle signs, and the image data of vehicle front face modeling of most models of 22 domestic mainstream brands were collected. Then, this article adopts the image classification processing method in computer vision to conduct car brand classification training on the database. Based on ResNet-8 and other model architectures, it trains and classifies the intelligent vehicle brand classification database with and without vehicle label. Finally, based on the shape coefficient, a 3D wireframe model and a curved surface model are obtained. The experimental results show that the 3D curve model can be obtained based on a single image from any angle, which greatly shortens the modeling period by 92%.

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The International Radiomics Platform – An Initiative of the German and Austrian Radiological Societies

RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren ◽

10.1055/a-1244-2775 ◽

2020 ◽

Author(s):

Daniel Overhoff ◽

Peter Kohlmann ◽

Alex Frydrychowicz ◽

Sergios Gatidis ◽

Christian Loewe ◽

...

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Image Data ◽

Quality Parameters ◽

Image Features ◽

Performance Criteria ◽

Data Sets ◽

Proof Of Concept ◽

Concept Study ◽

Artificial Intelligence Methods

Purpose The DRG-ÖRG IRP (Deutsche Röntgengesellschaft-Österreichische Röntgengesellschaft international radiomics platform) represents a web-/cloud-based radiomics platform based on a public-private partnership. It offers the possibility of data sharing, annotation, validation and certification in the field of artificial intelligence, radiomics analysis, and integrated diagnostics. In a first proof-of-concept study, automated myocardial segmentation and automated myocardial late gadolinum enhancement (LGE) detection using radiomic image features will be evaluated for myocarditis data sets. Materials and Methods The DRG-ÖRP IRP can be used to create quality-assured, structured image data in combination with clinical data and subsequent integrated data analysis and is characterized by the following performance criteria: Possibility of using multicentric networked data, automatically calculated quality parameters, processing of annotation tasks, contour recognition using conventional and artificial intelligence methods and the possibility of targeted integration of algorithms. In a first study, a neural network pre-trained using cardiac CINE data sets was evaluated for segmentation of PSIR data sets. In a second step, radiomic features were applied for segmental detection of LGE of the same data sets, which were provided multicenter via the IRP. Results First results show the advantages (data transparency, reliability, broad involvement of all members, continuous evolution as well as validation and certification) of this platform-based approach. In the proof-of-concept study, the neural network demonstrated a Dice coefficient of 0.813 compared to the expert's segmentation of the myocardium. In the segment-based myocardial LGE detection, the AUC was 0.73 and 0.79 after exclusion of segments with uncertain annotation.The evaluation and provision of the data takes place at the IRP, taking into account the FAT (fairness, accountability, transparency) and FAIR (findable, accessible, interoperable, reusable) criteria. Conclusion It could be shown that the DRG-ÖRP IRP can be used as a crystallization point for the generation of further individual and joint projects. The execution of quantitative analyses with artificial intelligence methods is greatly facilitated by the platform approach of the DRG-ÖRP IRP, since pre-trained neural networks can be integrated and scientific groups can be networked.In a first proof-of-concept study on automated segmentation of the myocardium and automated myocardial LGE detection, these advantages were successfully applied.Our study shows that with the DRG-ÖRP IRP, strategic goals can be implemented in an interdisciplinary way, that concrete proof-of-concept examples can be demonstrated, and that a large number of individual and joint projects can be realized in a participatory way involving all groups. Key Points: Citation Format

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Evaluation of Unsupervised Clustering Methods on Hyperspectral Image Data Sets

2018 IEEE International Conference on Progress in Informatics and Computing (PIC) ◽

10.1109/pic.2018.8706315 ◽

2018 ◽

Author(s):

Wei Zhang ◽

Zhichao Lian ◽

Chanying Huang

Keyword(s):

Hyperspectral Image ◽

Image Data ◽

Unsupervised Clustering ◽

Data Sets ◽

Clustering Methods ◽

Hyperspectral Image Data

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Fast Similarity Search in High Dimensional Image Data Sets

Proceedings of the 2nd international Conference on Big Data, Cloud and Applications ◽

10.1145/3090354.3090426 ◽

2017 ◽

Author(s):

Y. Hanyf ◽

H. Silkan

Keyword(s):

Similarity Search ◽

Image Data ◽

High Dimensional ◽

Data Sets ◽

Dimensional Image

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MondoA Drives B-ALL Malignancy through Enhanced Adaptation to Metabolic Stress

Blood ◽

10.1182/blood.2020007932 ◽

2021 ◽

Author(s):

Alexandra Sipol ◽

Erik Hameister ◽

Busheng Xue ◽

Julia Hofstetter ◽

Maxim Barenboim ◽

...

Keyword(s):

Stress Responses ◽

Lymphoblastic Leukemia ◽

Metabolic Stress ◽

Tca Cycle ◽

Underlying Mechanism ◽

Patient Data ◽

Data Sets ◽

Dependent Manner

Cancer cells are in most instances characterized by rapid proliferation and uncontrolled cell division. Hence, they must adapt to proliferation-induced metabolic stress through intrinsic or acquired anti-metabolic stress responses to maintain homeostasis and survival. One mechanism to achieve this is to reprogram gene expression in a metabolism-dependent manner. MondoA (also known as MLXIP), a member of the MYC interactome, has been described as an example of such a metabolic sensor. However, the role of MondoA in malignancy is not fully understood and the underlying mechanism in metabolic responses remains elusive. By assessing patient data sets we found that MondoA overexpression is associated with a worse survival in pediatric common acute lymphoblastic leukemia (B-ALL). Using CRISPR/Cas9 and RNA interference approaches, we observed that MondoA depletion reduces transformational capacity of B-ALL cells in vitro and dramatically inhibits malignant potential in an in vivo mouse model. Interestingly, reduced expression of MondoA in patient data sets correlated with enrichment in metabolic pathways. The loss of MondoA correlated with increased tricarboxylic acid (TCA) cycle activity. Mechanistically, MondoA senses metabolic stress in B-ALL cells by restricting oxidative phosphorylation through reduced PDH activity. Glutamine starvation conditions greatly enhance this effect and highlight the inability to mitigate metabolic stress upon loss of MondoA in B-ALL. Our findings give a novel insight into the function of MondoA in pediatric B-ALL and support the notion that MondoA inhibition in this entity offers a therapeutic opportunity and should be further explored.

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Automated Analysis of SEM X-Ray Spectral Images: A Powerful New Microanalysis Tool

Microscopy and Microanalysis ◽

10.1017/s1431927603030058 ◽

2003 ◽

Vol 9 (1) ◽

pp. 1-17 ◽

Cited By ~ 217

Author(s):

Paul G. Kotula ◽

Michael R. Keenan ◽

Joseph R. Michael

Keyword(s):

Image Data ◽

Large Data ◽

Automated Analysis ◽

Phase Identification ◽

Data Sets ◽

Multivariate Statistical ◽

X Ray ◽

Analysis Technique ◽

Statistical Analysis Technique ◽

Multi Phase

Spectral imaging in the scanning electron microscope (SEM) equipped with an energy-dispersive X-ray (EDX) analyzer has the potential to be a powerful tool for chemical phase identification, but the large data sets have, in the past, proved too large to efficiently analyze. In the present work, we describe the application of a new automated, unbiased, multivariate statistical analysis technique to very large X-ray spectral image data sets. The method, based in part on principal components analysis, returns physically accurate (all positive) component spectra and images in a few minutes on a standard personal computer. The efficacy of the technique for microanalysis is illustrated by the analysis of complex multi-phase materials, particulates, a diffusion couple, and a single-pixel-detection problem.

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Measuring the Morphology and Dynamics of the Snake River by Remote Sensing

The UW National Parks Service Research Station Annual Reports ◽

10.13001/uwnpsrc.2011.3773 ◽

2011 ◽

Vol 33 ◽

pp. 3-15

Author(s):

Carl Legleiter

Keyword(s):

Remote Sensing ◽

Image Data ◽

River System ◽

Field Work ◽

Hyperspectral Data ◽

Snake River ◽

Central Component ◽

Data Sets ◽

Channel Form ◽

And Behavior

The Snake River is a central component of Grand Teton National Park, and this dynamic fluvial system plays a key role in shaping the landscape and creating diverse aquatic and terrestrial habitat. The riverâs complexity and propensity for change make effective characterization of this resource difficult, however, and conventional, ground-based methods are simply inadequate. Remote sensing provides an appealing alternative approach that could facilitate resource management while providing novel insight on the factors controlling channel form and behavior. In this study, we evaluate the potential to measure the morphology and dynamics of a large, complex river system such as the Snake using optical image data. Initially, we made use of existing, publicly available images and basic digital aerial photography acquired in August 2010. Analysis to date has focused on estimating flow depths from these data, and preliminary results indicate that remote bathymetric mapping is feasible but not highly accurate, with important constraints related to the limited radiometric resolution of these data sets. Additional, more sophisticated hyperspectral data are scheduled for collection in 2011, along with further field work.

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An Automatic Feature Extraction Approach to Image Classification Using Genetic Programming

10.26686/wgtn.13158281.v1 ◽

2020 ◽

Author(s):

Ying Bi ◽

Bing Xue ◽

Mengjie Zhang

Keyword(s):

Feature Extraction ◽

Genetic Programming ◽

Image Classification ◽

Image Data ◽

Data Sets ◽

Automatic Feature Extraction ◽

Terminal Set ◽

Comparable Performance ◽

International Publishing ◽

High Level

© Springer International Publishing AG, part of Springer Nature 2018. Feature extraction is an essential process for image data dimensionality reduction and classification. However, feature extraction is very difficult and often requires human intervention. Genetic Programming (GP) can achieve automatic feature extraction and image classification but the majority of existing methods extract low-level features from raw images without any image-related operations. Furthermore, the work on the combination of image-related operators/descriptors in GP for feature extraction and image classification is limited. This paper proposes a multi-layer GP approach (MLGP) to performing automatic high-level feature extraction and classification. A new program structure, a new function set including a number of image operators/descriptors and two region detectors, and a new terminal set are designed in this approach. The performance of the proposed method is examined on six different data sets of varying difficulty and compared with five GP based methods and 42 traditional image classification methods. Experimental results show that the proposed method achieves better or comparable performance than these baseline methods. Further analysis on the example programs evolved by the proposed MLGP method reveals the good interpretability of MLGP and gives insight into how this method can effectively extract high-level features for image classification.

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AANMF: Attribute-Aware Attentional Neural Matrix Factorization

Information Technology And Control ◽

10.5755/j01.itc.48.4.23149 ◽

2019 ◽

Vol 48 (4) ◽

pp. 682-693

Author(s):

Bo Zheng ◽

Jinsong Hu

Keyword(s):

Matrix Factorization ◽

Recommendation System ◽

Auxiliary Information ◽

Inner Product ◽

Data Sets ◽

It Projects ◽

Real World Data ◽

Latent Space ◽

Almost All ◽

Novel Model

Matrix Factorization (MF) is one of the most intuitive and effective methods in the Recommendation System domain. It projects sparse (user, item) interactions into dense feature products which endues strong generality to the MF model. To leverage this interaction, recent works use auxiliary information of users and items. Despite effectiveness, irrationality still exists among these methods, since almost all of them simply add the feature of auxiliary information in dense latent space to the feature of the user or item. In this work, we propose a novel model named AANMF, short for Attribute-aware Attentional Neural Matrix Factorization. AANMF combines two main parts, namely, neural-network-based factorization architecture for modeling inner product and attention-mechanism-based attribute processing cell for attribute handling. Extensive experiments on two real-world data sets demonstrate the robust and stronger performance of our model. Notably, we show that our model can deal with the attributes of user or item more reasonably. Our implementation of AANMF is publicly available at https://github.com/Holy-Shine/AANMF.

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