scholarly journals Computer-aided lymph node segmentation in volumetric CT data

2012 ◽  
Vol 39 (9) ◽  
pp. 5419-5428 ◽  
Author(s):  
Reinhard R. Beichel ◽  
Yao Wang
Keyword(s):  
Lymph Node ◽  
Computer Aided ◽  
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

Contouring Medial Surface of Thin-Plate Structures Using Local Marching Cubes

2005 ◽  
Vol 5 (2) ◽  
pp. 111-115 ◽  
Author(s):  
Tomoyuki Fujimori ◽  
Hiromasa Suzuki ◽  
Yohei Kobayashi ◽  
Kiwamu Kase
Keyword(s):  
Thin Plate ◽  
Computer Aided Design ◽  
Prototype System ◽  
Marching Cubes ◽  
Plate Structures ◽  
Medial Surface ◽  
Surface Models ◽  
Marching Cubes Algorithm ◽  
Computer Aided ◽  
Ct Data

This paper describes a new algorithm for contouring a medial surface from CT (computed tomography) data of a thin-plate structure. Thin-plate structures are common in mechanical structures, such as car body shells. When designing thin-plate structures in CAD (computer-aided design) and CAE (computer-aided engineering) systems, their shapes are usually represented as surface models associated with their thickness values. In this research, we are aiming at extracting medial surface models of thin-plate structures from their CT data for use in CAD and CAE systems. Commonly used isosurfacing methods, such as marching cubes, are not applicable to contour the medial surface. Therefore, we first extract medial cells (cubes comprising eight neighboring voxels) from the CT data using a skeletonization method to apply the marching cubes algorithm for extracting the medial surface. It is not, however, guaranteed that the marching cubes algorithm can contour those medial cells (in short, not “marching cubeable”). In this study, therefore we developed cell operations that correct topological connectivity to guarantee such marching cubeability. We then use this method to assign virtual signs to the voxels to apply the marching cubes algorithm to generate triangular meshes of a medial surface and map the thicknesses of thin-plate structures to the triangle meshes as textures. A prototype system was developed to verify some experimental results.

The Use of MDCT-Based Computer-Aided Pathway Finding for Mediastinal and Perihilar Lymph Node Biopsy: A Randomized Controlled Prospective Trial

Respiration ◽  
2007 ◽  
Vol 74 (4) ◽  
pp. 423-431 ◽  
Author(s):  
Geoffrey McLennan ◽  
J. Scott Ferguson ◽  
Karl Thomas ◽  
Angela S. Delsing ◽  
Janice Cook-Granroth ◽  
...  
Keyword(s):  
Lymph Node ◽  
Prospective Trial ◽  
Lymph Node Biopsy ◽  
Randomized Controlled ◽  
Node Biopsy ◽  
Computer Aided

Computer-aided Surgical Planning for Implantation of Hearing Aids Based on CT Data in a VR Environment

Radiographics ◽  
2001 ◽  
Vol 21 (1) ◽  
pp. 183-190 ◽  
Author(s):  
Florian Dammann ◽  
Andreas Bode ◽  
Erwin Schwaderer ◽  
Michael Schaich ◽  
Martin Heuschmid ◽  
...  
Keyword(s):  
Hearing Aids ◽  
Surgical Planning ◽  
Computer Aided ◽  
Ct Data

Computer-aided lung nodule detection on high-resolution CT data

2002 ◽  
Author(s):  
Rafael Wiemker ◽  
Patrick Rogalla ◽  
Andre Zwartkruis ◽  
Thomas Blaffert
Keyword(s):  
High Resolution ◽  
Lung Nodule ◽  
Lung Nodule Detection ◽  
High Resolution Ct ◽  
Nodule Detection ◽  
Computer Aided ◽  
Ct Data

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.

scholarly journals Use of 68Ga-PSMA-11 and 18F-FDG PET-CT Dual-Tracer to Differentiate Between Lymph Node Metastases and Ganglia

2021 ◽  
Vol 11 ◽  
Author(s):  
Yiping Shi ◽  
Lian Xu ◽  
Yinjie Zhu ◽  
Yining Wang ◽  
Ruohua Chen ◽  
...  
Keyword(s):  
Lymph Node ◽  
Lymph Node Metastases ◽  
Fdg Pet ◽  
Standardized Uptake Value ◽  
Fdg Uptake ◽  
Positron Emission ◽  
Pet Ct ◽  
Node Metastases ◽  
Ct Data ◽  
Fdg Pet Ct

PurposeDifferentiating lymph node metastases (LNM) from peripheral ganglia by physiological prostate-specific membrane antigen (PSMA) uptake is challenging. Two tracers (68Ga-PSMA-11 and 18F-fluorodeoxyglucose [FDG]) metabolic uptake patterns were evaluated by positron emission tomography-computed tomography (PET-CT), searching for differences that could tell ganglia from LNM.MethodsDual 68Ga-PSMA-11 and 18F-FDG PET-CT data of 138 prostate cancer patients acquired from June 2018 to December 2019 were retrospectively evaluated. Ganglia and LNM with PSMA-11 uptake above local background were analyzed by the location and PSMA-11-PET and FDG-PET maximum standardized uptake value (SUVmax).ResultsPSMA-11-positive ganglia (n = 381) and LNM (n = 83) were identified in 138 and 58 patients, respectively. The LNM SUVmax of PSMA-11-PET (16.4 ± 14.8 vs 2.3 ± 0.7, P < 0.001) and FDG-PET (3.3 ± 3.2 vs 1.5 ± 0.5, P < 0.001) were higher than in ganglia. The probabilities of being an LNM in the low-potential (PSMA-11-PET SUVmax of <4.1 and FDG-PET SUVmax of <2.05), moderate-potential (PSMA-11-PET SUVmax of >4.1 and FDG-PET SUVmax of <2.05, or PSMA-11-PET SUVmax of <4.1 and FDG-PET SUVmax of >2.05), and high-potential (PSMA-11-PET SUVmax of >4.1 and FDG-PET SUVmax of >2.05) groups were 0.9% (3/334), 44.6% (37/83), and 91.5% (43/47), respectively (P < 0.001). The cervical and coeliac ganglia had higher PSMA-11 and FDG uptake than the sacral ganglia (P < 0.001 for all). LNM PSMA-11 and FDG uptake was similar in these three locations.ConclusionThe FDG-PET and PSMA-11-PET SUVmax, especially when combined, could well differentiate LNM from ganglia. The tracers uptake differed between cervical/coeliac and sacral ganglia, so the lesion location should be considered during image assessment.

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

Computer-aided abdominal lymph node detection using contrast-enhanced CT images

2011 ◽  
Author(s):  
Jiamin Liu ◽  
Jeremy Hua ◽  
Jianhua Yao ◽  
Jacob M. White ◽  
Ronald M. Summers
Keyword(s):  
Lymph Node ◽  
Ct Images ◽  
Abdominal Lymph Node ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Computer Aided
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