Extraction and classification of 3D objects from volumetric CT data

2016 ◽  
Author(s):  
Samuel M. Song ◽  
Junghyun Kwon ◽  
Austin Ely ◽  
John Enyeart ◽  
Chad Johnson ◽  
...  
Keyword(s):  
3D Objects ◽  
Volumetric Ct ◽  
Ct Data

Middle and Inner Ear: Improved Depiction with Multiplanar Reconstruction of Volumetric CT Data

Radiographics ◽  
2006 ◽  
Vol 26 (1) ◽  
pp. 115-124 ◽  
Author(s):  
John I. Lane ◽  
E. Paul Lindell ◽  
Robert J. Witte ◽  
David R. DeLone ◽  
Colin L. W. Driscoll
Keyword(s):  
Inner Ear ◽  
Multiplanar Reconstruction ◽  
Volumetric Ct ◽  
Ct Data

scholarly journals Deep Learning Method for Quantitative Analysis of Covid -19 using CT Chest Images

2021 ◽  
Author(s):  
Indrajeet Kumar ◽  
Jyoti Rawat
Keyword(s):  
Deep Learning ◽  
Diagnostic Tests ◽  
Ct Images ◽  
Rapid Diagnosis ◽  
Chest Ct ◽  
Visual Features ◽  
Learning Module ◽  
Volumetric Ct ◽  
The Cost

Abstract The manual diagnostic tests performed in laboratories for pandemic disease such as COVID19 is time-consuming, requires skills and expertise of the performer to yield accurate results. Moreover, it is very cost ineffective as the cost of test kits is high and also requires well-equipped labs to conduct them. Thus, other means of diagnosing the patients with presence of SARS-COV2 (the virus responsible for COVID19) must be explored. A radiography method like chest CT images is one such means that can be utilized for diagnosis of COVID19. The radio-graphical changes observed in CT images of COVID19 patient helps in developing a deep learning-based method for extraction of graphical features which are then used for automated diagnosis of the disease ahead of laboratory-based testing. The proposed work suggests an Artificial Intelligence (AI) based technique for rapid diagnosis of COVID19 from given volumetric CT images of patient’s chest by extracting its visual features and then using these features in the deep learning module. The proposed convolutional neural network is deployed for classifying the infectious and non-infectious SARS-COV2 subjects. The proposed network utilizes 746 chests scanned CT images of which 349 images belong to COVID19 positive cases while remaining 397 belong negative cases of COVID19. The extensive experiment has been completed with the accuracy of 98.4 %, sensitivity of 98.5 %, the specificity of 98.3 %, the precision of 97.1 %, F1score of 97.8 %. The obtained result shows the outstanding performance for classification of infectious and non-infectious for COVID19 cases.

scholarly journals PET Index of Bone Glucose Metabolism (PIBGM) Classification of PET/CT Data for Fever of Unknown Origin Diagnosis

PLoS ONE ◽  
2015 ◽  
Vol 10 (6) ◽  
pp. e0130173 ◽  
Author(s):  
Jian Yang ◽  
Xinxin Liu ◽  
Danni Ai ◽  
Jingfan Fan ◽  
Youjing Zheng ◽  
...  
Keyword(s):  
Glucose Metabolism ◽  
Fever Of Unknown Origin ◽  
Unknown Origin ◽  
Pet Ct ◽  
Ct Data ◽  
Pet Index

Robust automated detection, segmentation, and classification of hepatic tumors from CT data

2012 ◽  
Author(s):  
Marius George Linguraru ◽  
William J. Richbourg ◽  
Vivek Pamulapati ◽  
Shijun Wang ◽  
Ronald M. Summers
Keyword(s):  
Automated Detection ◽  
Hepatic Tumors ◽  
Ct Data

scholarly journals New Approaches to Systematization and Treatment of Impression Monocondylar Tibial Plateau Fractures

2016 ◽  
Vol 23 (3) ◽  
pp. 16-22
Author(s):  
M. V Gilev ◽  
E. A Volokitina ◽  
Yu. V Antoniadi ◽  
S. M Kutepov
Keyword(s):  
Tibial Plateau ◽  
Main Group ◽  
Tibial Plateau Fractures ◽  
Control Group ◽  
Treatment Results ◽  
Operational Classification ◽  
Knee Contracture ◽  
Secondary Displacement ◽  
Ct Data

Treatment results for 109 patients (mean age 56 ± 1.7 years) with monocondylar impression tibial plateau fractures (ITPF) are presented. Patients from the control group (n=63) were operated on during the period from 2008 to 1010, patients from the main group (n=46) - from 2011 to 2013. Patients from the main group were treated with regard for injury localization relative to plateau center according to proposed operational classification of impression fractures (by CT data) and algorithm to choose the osteosynthesis technique depending on the anatomic and morphologic peculiarities of the intra-articular injury. In patients from the main group the evaluation by Rasmussen scale 36 months after intervention showed excellent results in 15 (38.4%), good - in 22 (56.4%), satisfactory - in 5 (12.8%) of patients, no poor results were recorded, and in patients from the control group - in 6 (11.5%), 8 (15.3%), 36 (69.3%) and 3 (5,8%) patients, respectively. Three (7.6%) complications (secondary displacement of fragments (2), knee contracture (1)) were observed in the main group, and 11 (20.9%) in the control group (20.9%) - local infectious inflammatory process (4), secondary displacement of plateau fragments (6), condylar sag (1). Perfected tactics of treatment of patients with ITPF enabled to achieve 3.48 times more excellent and good results (p

scholarly journals Cascaded Dilated Deep Residual Network for Volumetric Liver Segmentation From CT Image

2021 ◽  
Vol 12 (1) ◽  
pp. 34-45
Author(s):  
Gajendra Kumar Mourya ◽  
Manashjit Gogoi ◽  
S. N. Talbar ◽  
Prasad Vilas Dutande ◽  
Ujjwal Baid
Keyword(s):  
Evaluation System ◽  
Automatic Segmentation ◽  
Input Image ◽  
Liver Segmentation ◽  
Residual Network ◽  
Online Evaluation ◽  
Volumetric Ct ◽  
Ct Data ◽  
Symmetric Surface ◽  
Evaluation Parameters

Volumetric liver segmentation is a prerequisite for liver transplantation and radiation therapy planning. In this paper, dilated deep residual network (DDRN) has been proposed for automatic segmentation of liver from CT images. The combination of three parallel DDRN is cascaded with fourth DDRN in order to get final result. The volumetric CT data of 40 subjects belongs to “Combined Healthy Abdominal Organ Segmentation” (CHAOS) challenge 2019 is utilized to evaluate the proposed method. Input image converted into three images using windowing ranges and fed to three DDRN. The output of three DDRN along with original image fed to the fourth DDRN as an input. The output of cascaded network is compared with the three parallel DDRN individually. Obtained results were quantitatively evaluated with various evaluation parameters. The results were submitted to online evaluation system, and achieved average dice coefficient is 0.93±0.02; average symmetric surface distance (ASSD) is 4.89±0.91. In conclusion, obtained results are prominent and consistent.

Dynamic Volumetric CT Angiography for the Detection and Classification of Endoleaks: Application of Cine Imaging Using a 320-Row CT Scanner with 16-cm Detectors

2014 ◽  
Vol 25 (8) ◽  
pp. 1172-1180.e1 ◽  
Author(s):  
Yuya Koike ◽  
Kazufumi Ishida ◽  
Soichiro Hase ◽  
Yasuyuki Kobayashi ◽  
Jun-ichi Nishimura ◽  
...  
Keyword(s):  
Ct Angiography ◽  
Cine Imaging ◽  
Ct Scanner ◽  
Volumetric Ct

Exploring technical issues in personalized medicine: NSCLC survival prediction by quantitative image analysis—usefulness of density correction of volumetric CT data

2020 ◽  
Vol 125 (7) ◽  
pp. 625-635 ◽  
Author(s):  
Alessandra Farchione ◽  
Anna Rita Larici ◽  
Carlotta Masciocchi ◽  
Giuseppe Cicchetti ◽  
Maria Teresa Congedo ◽  
...  
Keyword(s):  
Image Analysis ◽  
Personalized Medicine ◽  
Quantitative Image Analysis ◽  
Survival Prediction ◽  
Density Correction ◽  
Quantitative Image ◽  
Technical Issues ◽  
Volumetric Ct ◽  
Ct Data
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