Segmentation of Costal Cartilage in Abdominal CT Data using Watershed Markers

2007 ◽  
Author(s):  
Andrew B. Holbrook ◽  
Kim Butts Pauly
Keyword(s):  
Costal Cartilage ◽  
Abdominal Ct ◽  
Ct Data

Artifacts in Slab Average-Intensity-Projection Images Reformatted from JPEG 2000 Compressed Thin-Section Abdominal CT Data Sets

2008 ◽  
Vol 190 (6) ◽  
pp. W342-W350 ◽  
Author(s):  
Bohyoung Kim ◽  
Kyoung Ho Lee ◽  
Kil Joong Kim ◽  
Rafal Mantiuk ◽  
Hye-ri Kim ◽  
...  
Keyword(s):  
Thin Section ◽  
Average Intensity ◽  
Data Sets ◽  
Jpeg 2000 ◽  
Abdominal Ct ◽  
Average Intensity Projection ◽  
Projection Images ◽  
Ct Data

scholarly journals Automatic multi-object organ detection and segmentation in abdominal CT-data

2020 ◽  
Author(s):  
Oliver Mietzner ◽  
Andre Mastmeyer
Keyword(s):  
Virtual Reality ◽  
Random Regression ◽  
Ct Scans ◽  
Virtual Reality Simulation ◽  
Abdominal Ct ◽  
Abdominal Organs ◽  
Ct Data ◽  
Bounding Boxes

AbstractThe ability to generate 3D patient models in a fast and reliable way, is of great importance, e.g. for the simulation of liver punctures in a virtual reality simulation [1], [2], [3], [4]. The aim is to automatically detect and segment abdominal structures in CT-scans. In particular among the selected organ group, the pancreas poses a challenge. We use a combination of random regression forests and U-Nets to detect bounding boxes and generate segmentation masks for five abdominal organs (liver, kidneys, spleen, pancreas). Training and testing is carried out on 50 CT-scans from various public sources. The results show Dice coefficients of up to 0.71.

Semi-automatic 3D segmentation of costal cartilage in CT data from Pectus Excavatum patients

2015 ◽  
Author(s):  
Daniel Barbosa ◽  
Sandro Queirós ◽  
Nuno Rodrigues ◽  
Jorge Correia-Pinto ◽  
J. Vilaça
Keyword(s):  
Pectus Excavatum ◽  
Costal Cartilage ◽  
3D Segmentation ◽  
Ct Data

scholarly journals Using Abdominal CT Data for Visceral Fat Evaluation

2013 ◽  
Vol 59 (5) ◽  
pp. 254-256 ◽  
Author(s):  
Pop M ◽  
Pop Raluca
Keyword(s):  
Visceral Fat ◽  
Human Error ◽  
Standard Procedure ◽  
Fat Distribution ◽  
Diagnosis And Treatment ◽  
Fat Percentage ◽  
Abdominal Ct ◽  
Visceral Fat Area ◽  
Significant Difference ◽  
Ct Data

Abstract Background: Quantitative assessment of body fat is important for the diagnosis and treatment of diseases related to obesity, Computed tomography (CT) becoming the standard procedure for measuring the abdominal fat distribution. Material and method: The retrospective study included 111 inpatients, who underwent routine abdominal CT exams in the Radiology Laboratory of SCJU Tg.Mures (2013). MPR MDCT (SOMATOM AS 64) data was processed using a custom written MATLAB R2009b software, ImageJ being used for tracing of the visceral fat area (VFA). Patient data (including blood glucose, cholesterol and triglycerides) were analyzed using MO Excel and GraphPad Inprism5. Results: Visceral Fat percentage varied in population from 14.59-68.69 (SD = 11.83) with significant difference between sexes (male vs. female, 46.98 vs. 31.62, p <0.05). Cholesterol values >220 mg% and triglycerides >150 mg% are significantly associated with the VF percent (p <0.05). Overall there is a weak correlation between the lab variables and the measured fat, the strongest one being between triglycerides and the VFA (r = +0.23) and between age and VFA percentage (certain samples). Conclusions: The technique used should decreases the human error in marking of the fat areas providing a better estimation of the VF/VF percentage. CT measured VF relates with certain lab tests. Further analysis, is required for a better use of CT in obesity related pathology diagnosis and treatment

Validation of lesion simulations in clinical CT data for anonymized chest and abdominal CT databases

2019 ◽  
Vol 46 (4) ◽  
pp. 1931-1937 ◽  
Author(s):  
Marthony Robins ◽  
Justin Solomon ◽  
Lynne M. Hurwitz Koweek ◽  
Jared Christensen ◽  
Ehsan Samei
Keyword(s):  
Abdominal Ct ◽  
Ct Data

Blinded re-analysis of CT-data with HeartNavigator®: Improved TAVI planning versus TEE and CT alone

2013 ◽  
Vol 61 (S 01) ◽  
Author(s):  
C Schmidtke ◽  
D Richardt ◽  
M Strauch ◽  
J Barkhausen ◽  
HH Sievers ◽  
...  
Keyword(s):  
Ct Data

Effect of CT-based attenuation correction on uptake ratios in skeletal SPECT

2007 ◽  
Vol 46 (01) ◽  
pp. 38-42 ◽  
Author(s):  
V. Schulz ◽  
I. Nickel ◽  
A. Nömayr ◽  
A. H. Vija ◽  
C. Hocke ◽  
...  
Keyword(s):  
Vertebral Body ◽  
Facet Joint ◽  
One Dimension ◽  
Patient Specific ◽  
Data Sets ◽  
Facet Joints ◽  
Lumbar Vertebral Body ◽  
Ct System ◽  
Ct Data ◽  
Attenuation Corrected

SummaryThe aim of this study was to determine the clinical relevance of compensating SPECT data for patient specific attenuation by the use of CT data simultaneously acquired with SPECT/CT when analyzing the skeletal uptake of polyphosphonates (DPD). Furthermore, the influence of misregistration between SPECT and CT data on uptake ratios was investigated. Methods: Thirty-six data sets from bone SPECTs performed on a hybrid SPECT/CT system were retrospectively analyzed. Using regions of interest (ROIs), raw counts were determined in the fifth lumbar vertebral body, its facet joints, both anterior iliacal spinae, and of the whole transversal slice. ROI measurements were performed in uncorrected (NAC) and attenuation-corrected (AC) images. Furthermore, the ROI measurements were also performed in AC scans in which SPECT and CT images had been misaligned by 1 cm in one dimension beforehand (ACX, ACY, ACZ). Results: After AC, DPD uptake ratios differed significantly from the NAC values in all regions studied ranging from 32% for the left facet joint to 39% for the vertebral body. AC using misaligned pairs of patient data sets led to a significant change of whole-slice uptake ratios whose differences ranged from 3,5 to 25%. For ACX, the average left-to-right ratio of the facet joints was by 8% and for the superior iliacal spines by 31% lower than the values determined for the matched images (p <0.05). Conclusions: AC significantly affects DPD uptake ratios. Furthermore, misalignment between SPECT and CT may introduce significant errors in quantification, potentially also affecting leftto- right ratios. Therefore, at clinical evaluation of attenuation- corrected scans special attention should be given to possible misalignments between SPECT and CT.

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