Working with large 2D/3D data sets

2013 ◽  
pp. 201-236
Author(s):  
Alexey Boreskov ◽  
Evgeniy Shikin
Keyword(s):  
Data Sets ◽  
3D Data

Direct phase determination in electron crystallography: small organic molecules

1992 ◽  
Vol 50 (2) ◽  
pp. 1166-1167
Author(s):  
Douglas L. Dorset
Keyword(s):  
Organic Molecules ◽  
Data Sets ◽  
Temperature Structure ◽  
3D Analysis ◽  
Intensity Data ◽  
Electron Crystallography ◽  
Phase Determination ◽  
Measured Intensity ◽  
3D Data

The quantitative use of electron diffraction intensity data for the determination of crystal structures represents the pioneering achievement in the electron crystallography of organic molecules, an effort largely begun by B. K. Vainshtein and his co-workers. However, despite numerous representative structure analyses yielding results consistent with X-ray determination, this entire effort was viewed with considerable mistrust by many crystallographers. This was no doubt due to the rather high crystallographic R-factors reported for some structures and, more importantly, the failure to convince many skeptics that the measured intensity data were adequate for ab initio structure determinations.We have recently demonstrated the utility of these data sets for structure analyses by direct phase determination based on the probabilistic estimate of three- and four-phase structure invariant sums. Examples include the structure of diketopiperazine using Vainshtein's 3D data, a similar 3D analysis of the room temperature structure of thiourea, and a zonal determination of the urea structure, the latter also based on data collected by the Moscow group.

Time consumption and quality of an automated fusion tool for SPECT and MRI images of the brain

2003 ◽  
Vol 42 (05) ◽  
pp. 215-219
Author(s):  
G. Platsch ◽  
A. Schwarz ◽  
K. Schmiedehausen ◽  
B. Tomandl ◽  
W. Huk ◽  
...  
Keyword(s):  
Data Sets ◽  
3D Mri ◽  
Clinical Routine ◽  
Fusion Procedure ◽  
3D Data ◽  
Mri Scans ◽  
2D Data ◽  
Time Required ◽  
The Brain ◽  
Manual Correction

Summary: Aim: Although the fusion of images from different modalities may improve diagnostic accuracy, it is rarely used in clinical routine work due to logistic problems. Therefore we evaluated performance and time needed for fusing MRI and SPECT images using a semiautomated dedicated software. Patients, material and Method: In 32 patients regional cerebral blood flow was measured using 99mTc ethylcystein dimer (ECD) and the three-headed SPECT camera MultiSPECT 3. MRI scans of the brain were performed using either a 0,2 T Open or a 1,5 T Sonata. Twelve of the MRI data sets were acquired using a 3D-T1w MPRAGE sequence, 20 with a 2D acquisition technique and different echo sequences. Image fusion was performed on a Syngo workstation using an entropy minimizing algorithm by an experienced user of the software. The fusion results were classified. We measured the time needed for the automated fusion procedure and in case of need that for manual realignment after automated, but insufficient fusion. Results: The mean time of the automated fusion procedure was 123 s. It was for the 2D significantly shorter than for the 3D MRI datasets. For four of the 2D data sets and two of the 3D data sets an optimal fit was reached using the automated approach. The remaining 26 data sets required manual correction. The sum of the time required for automated fusion and that needed for manual correction averaged 320 s (50-886 s). Conclusion: The fusion of 3D MRI data sets lasted significantly longer than that of the 2D MRI data. The automated fusion tool delivered in 20% an optimal fit, in 80% manual correction was necessary. Nevertheless, each of the 32 SPECT data sets could be merged in less than 15 min with the corresponding MRI data, which seems acceptable for clinical routine use.

Increasing axial resolution of 3D data sets using deconvolution algorithms

2011 ◽  
Vol 243 (3) ◽  
pp. 293-302 ◽  
Author(s):  
P. TOPOR ◽  
M. ZIMANYI ◽  
A. MATEASIK
Keyword(s):  
Data Sets ◽  
Axial Resolution ◽  
3D Data

BrainView: a computer program for reconstruction and interactive visualization of 3D data sets

1998 ◽  
Vol 84 (1-2) ◽  
pp. 143-154 ◽  
Author(s):  
Klaudia Lohmann ◽  
Eckart D Gundelfinger ◽  
Henning Scheich ◽  
Rita Grimm ◽  
Wolfgang Tischmeyer ◽  
...  
Keyword(s):  
Computer Program ◽  
Interactive Visualization ◽  
Data Sets ◽  
3D Data

Diagnosis of COVID-19 using 3D CT scans and vaccination for COVID-19

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Gangadhar Ch ◽  
S. Jana ◽  
Sankararao Majji ◽  
Prathyusha Kuncha ◽  
Fantin Irudaya Raj E. ◽  
...  
Keyword(s):  
Virus Isolate ◽  
Research Work ◽  
Three Dimensional ◽  
Ct Scans ◽  
Data Sets ◽  
Content Type ◽  
Infected People ◽  
3D Data ◽  
Ct Classification ◽  
First Time

Purpose For the first time in a decade, a new form of pneumonia virus, coronavirus, COVID-19, appeared in Wuhan, China. To date, it has affected millions of people, killed thousands and resulted in thousands of deaths around the world. To stop the spread of this virus, isolate the infected people. Computed tomography (CT) imaging is very accurate in revealing the details of the lungs and allows oncologists to detect COVID. However, the analysis of CT scans, which can include hundreds of images, may cause delays in hospitals. The use of artificial intelligence (AI) in radiology could help to COVID-19-positive cancer in this manner is the main purpose of the work. Design/methodology/approach CT scans are a medical imaging procedure that gives a three-dimensional (3D) representation of the lungs for clinical purposes. The volumetric 3D data sets can be regarded as axial, coronal and transverse data sets. By using AI, we can diagnose the virus presence. Findings The paper discusses the use of an AI for COVID-19, and CT classification issue and vaccination details of COVID-19 have been detailed in this paper. Originality/value Originality of the work is, all the data can be collected genuinely and did research work doneown methodology.

Prevalence and risk factors of root resorption of adjacent teeth in maxillary canine impaction, among untreated children and adolescents

2018 ◽  
Vol 41 (5) ◽  
pp. 447-453 ◽  
Author(s):  
Frédéric Rafflenbeul ◽  
Catherine-Isabelle Gros ◽  
François Lefebvre ◽  
Sophie Bahi-Gross ◽  
Raphaëlle Maizeray ◽  
...  
Keyword(s):  
Risk Factors ◽  
Children And Adolescents ◽  
Root Resorption ◽  
Significant Risk ◽  
Significant Risk Factor ◽  
Data Sets ◽  
Maxillary Canine ◽  
3D Data ◽  
Potential Risk Factors ◽  
Associated Risk Factors

Summary Objectives The aim of this retrospective study was to assess in maxillary canine impaction cases both the prevalence of root resorption of adjacent teeth among untreated children and adolescents, and its associated risk factors. Subjects and methods Sixty subjects (mean age 12.2 years; SD 1.9; range 8–17 years) with 83 displaced maxillary canines and without any past or ongoing orthodontic treatment were included in this study. The presence of root resorption was evaluated on images from a single cone beam computed tomography (CBCT) unit. Potential risk factors were measured on the CBCT images and on panoramic reconstructions of the 3D data sets. The sample was characterized by descriptive statistics and multiple logistic regressions were performed to predict root resorption. Results Root resorption of at least one adjacent tooth was detected in 67.5 per cent of the affected quadrants. It was found that 55.7 per cent of the lateral incisors, 8.4 per cent of the central incisors, and 19.5 per cent of first premolars were resorbed. Of the detected resorptions, 71.7 per cent were considered slight, 14.9 per cent moderate, and 13.4 per cent severe. Contact between the displaced canine(s) and the adjacent teeth roots was the only identified statistically significant risk factor, all teeth being considered (odds ratio [OR] = 18.7, 95% confidence interval: 2.26–756, P < 0.01). An enlarged canine dental follicle, a peg upper lateral, or an upper lateral agenesis were not significantly associated with root resorption of adjacent teeth, nor were age nor gender. Conclusions Root resorption of adjacent teeth was detected in more than two-thirds of a sample of sixty untreated children and adolescents.

A Web Service-based Grid Portal for Edgebreaker Compression

2005 ◽  
Vol 44 (02) ◽  
pp. 233-238 ◽  
Author(s):  
M. C. Barba ◽  
E. Blasi ◽  
M. Cafaro ◽  
S. Fiore ◽  
M. Mirto ◽  
...  
Keyword(s):  
Web Services ◽  
Web Service ◽  
Magnetic Resonance Images ◽  
Computational Grids ◽  
Data Sets ◽  
Remote Visualization ◽  
Compression Technique ◽  
Grid Portal ◽  
3D Data ◽  
Novel Approach

Summary Background: In health applications, and elsewhere, 3D data sets are increasingly accessed through the Internet. To reduce the transfer time while maintaining an unaltered 3D model, adequate compression and decompression techniques are needed. Recently, Grid technologies have been integrated with Web Services technologies to provide a framework for interoperable application-to-application interaction. Objectives: The paper describes an implementation of the Edgebreaker compression technique exploiting web services technology and presents a novel approach for using such services in a Grid Portal. The Grid portal, developed at the CACT/ISUFI of the University of Lecce, allows the processing and delivery of biomedical images (CT – computerized tomography – and MRI – magnetic resonance images) in a distributed environment, using the power and security of computational Grids. Methods: The Edgebreaker Compression Web Service has been deployed on a Grid portal and allows compressing and decompressing 3D data sets using the Globus toolkit GSI (Globus Security Infrastructure) protocol. Moreover, the classical algorithm has been modified extending the compression to files containing more than one object. Results and Conclusions: An implementation of the Edgebreaker compression technique and related experimental results are presented. A novel approach for using the compression web service in a Grid portal allowing storing and preprocessing of huge 3D data sets, and subsequent efficient transmission of results for remote visualization is also described.

scholarly journals PUB-SalNet: A Pre-Trained Unsupervised Self-Aware Backpropagation Network for Biomedical Salient Segmentation

Algorithms ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 126 ◽  
Author(s):  
Feiyang Chen ◽  
Ying Jiang ◽  
Xiangrui Zeng ◽  
Jing Zhang ◽  
Xin Gao ◽  
...  
Keyword(s):  
Training Data ◽  
Data Sets ◽  
Local Similarity ◽  
Biomedical Data ◽  
Data Set ◽  
Biomedical Image ◽  
Feature Representations ◽  
3D Data ◽  
Supervised Methods ◽  
Data Driven Decisions

Salient segmentation is a critical step in biomedical image analysis, aiming to cut out regions that are most interesting to humans. Recently, supervised methods have achieved promising results in biomedical areas, but they depend on annotated training data sets, which requires labor and proficiency in related background knowledge. In contrast, unsupervised learning makes data-driven decisions by obtaining insights directly from the data themselves. In this paper, we propose a completely unsupervised self-aware network based on pre-training and attentional backpropagation for biomedical salient segmentation, named as PUB-SalNet. Firstly, we aggregate a new biomedical data set from several simulated Cellular Electron Cryo-Tomography (CECT) data sets featuring rich salient objects, different SNR settings, and various resolutions, which is called SalSeg-CECT. Based on the SalSeg-CECT data set, we then pre-train a model specially designed for biomedical tasks as a backbone module to initialize network parameters. Next, we present a U-SalNet network to learn to selectively attend to salient objects. It includes two types of attention modules to facilitate learning saliency through global contrast and local similarity. Lastly, we jointly refine the salient regions together with feature representations from U-SalNet, with the parameters updated by self-aware attentional backpropagation. We apply PUB-SalNet for analysis of 2D simulated and real images and achieve state-of-the-art performance on simulated biomedical data sets. Furthermore, our proposed PUB-SalNet can be easily extended to 3D images. The experimental results on the 2d and 3d data sets also demonstrate the generalization ability and robustness of our method.

scholarly journals Reconstruction of Axial Tomographic High Resolution Data from Confocal Fluorescence Microscopy: A Method for Improving 3D FISH Images

2000 ◽  
Vol 20 (1) ◽  
pp. 7-15 ◽  
Author(s):  
R. Heintzmann ◽  
G. Kreth ◽  
C. Cremer
Keyword(s):  
High Resolution ◽  
Laser Scanning ◽  
Axial Direction ◽  
Laser Scanning Microscopy ◽  
Three Dimensions ◽  
Confocal Laser ◽  
Data Sets ◽  
Data Set ◽  
High Resolution Data ◽  
3D Data

Fluorescent confocal laser scanning microscopy allows an improved imaging of microscopic objects in three dimensions. However, the resolution along the axial direction is three times worse than the resolution in lateral directions. A method to overcome this axial limitation is tilting the object under the microscope, in a way that the direction of the optical axis points into different directions relative to the sample. A new technique for a simultaneous reconstruction from a number of such axial tomographic confocal data sets was developed and used for high resolution reconstruction of 3D‐data both from experimental and virtual microscopic data sets. The reconstructed images have a highly improved 3D resolution, which is comparable to the lateral resolution of a single deconvolved data set. Axial tomographic imaging in combination with simultaneous data reconstruction also opens the possibility for a more precise quantification of 3D data. The color images of this publication can be accessed from http://www.esacp.org/acp/2000/20‐1/heintzmann.htm. At this web address an interactive 3D viewer is additionally provided for browsing the 3D data. This java applet displays three orthogonal slices of the data set which are dynamically updated by user mouse clicks or keystrokes.

Registration of anatomical and functional 3D-data sets

1992 ◽  
Author(s):  
Hanna Pohjonen ◽  
Aaro Kiuru ◽  
Paivi Nikkinen ◽  
Pekka Karp ◽  
Juha Yla-Jaaski ◽  
...  
Keyword(s):  
Data Sets ◽  
3D Data
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