CURVATURE-BASED CORRECTION ALGORITHM FOR AUTOMATIC LUNG SEGMENTATION ON CHEST CT IMAGES

2014 ◽  
Vol 22 (01) ◽  
pp. 1-28 ◽  
Author(s):  
SHICHENG HU ◽  
KESEN BI ◽  
QUANXU GE ◽  
MINGCHAO LI ◽  
XIN XIE ◽  
...  
Keyword(s):  
Ct Images ◽  
Chest Ct ◽  
Smoothing Algorithm ◽  
Correction Algorithm ◽  
Lung Segmentation ◽  
Preliminary Step ◽  
Chest Computed Tomography ◽  
Optimal Values ◽  
Selection Of

In order to ameliorate the lung defects caused by missed juxtapleural nodules in lung segmentation on chest computed tomography (CT) images, we develop a Newton–Cotes-based smoothing algorithm (NCBS) which is used as a preliminary step to remove noises as many as possible. Next considering the crescent outline features of the lung, we propose a curvature-based correction algorithm (CBC) for the determination of the correction threshold. The application of the proposed algorithms is demonstrated in the process of lung segmentation and the experimental results on 25 real datasets are illustrated. Furthermore, some experiments are conducted to investigate the effects of the key parameters in CBC on the performances of lung segmentation so as to decide their optimal values. In addition, the CBC is compared with other methods analytically and experimentally. The overall results show that our proposed algorithm in lung segmentation excels the related methods on the capability of automatic selection of the correction threshold, as well as the performances of accuracy, efficiency and feasibility.

scholarly journals A Visually Guided Framework for Lung Segmentation and Visualization in Chest CT Images

2018 ◽  
Vol 8 (3) ◽  
pp. 485-493 ◽  
Author(s):  
Shouren Lan ◽  
Xin Liu ◽  
Lisheng Wang ◽  
Chaoyi Cui
Keyword(s):  
Ct Images ◽  
Chest Ct ◽  
Visually Guided ◽  
Lung Segmentation

Pathological lung segmentation in chest CT images based on improved random walker

2020 ◽  
pp. 105864
Author(s):  
Cheng Chen ◽  
Ruoxiu Xiao ◽  
Tao Zhang ◽  
Yuanyuan Lu ◽  
Xiaoyu Guo ◽  
...  
Keyword(s):  
Ct Images ◽  
Chest Ct ◽  
Lung Segmentation ◽  
Random Walker

scholarly journals Adaptive border marching algorithm: Automatic lung segmentation on chest CT images

2008 ◽  
Vol 32 (6) ◽  
pp. 452-462 ◽  
Author(s):  
Jiantao Pu ◽  
Justus Roos ◽  
Chin A. Yi ◽  
Sandy Napel ◽  
Geoffrey D. Rubin ◽  
...  
Keyword(s):  
Ct Images ◽  
Chest Ct ◽  
Lung Segmentation

scholarly journals Chest CT Images for COVID-19: Radiologists and Computer-based detection

2020 ◽  
Author(s):  
Qingli Dou ◽  
Jiangping Liu ◽  
Wenwu Zhang ◽  
Yanan Gu ◽  
Wan-Ting Hsu ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Analysis ◽  
Ct Images ◽  
Chest Ct ◽  
Image Analysis System ◽  
Ct Image ◽  
Chest Computed Tomography ◽  
Computer Based ◽  
Analysis System ◽  
Novel Coronavirus

ABSTRACTBackgroundCharacteristic chest computed tomography (CT) manifestation of 2019 novel coronavirus (COVID-19) was added as a diagnostic criterion in the Chinese National COVID-19 management guideline. Whether the characteristic findings of Chest CT could differentiate confirmed COVID-19 cases from other positive nucleic acid test (NAT)-negative patients has not been rigorously evaluated.PurposeWe aim to test whether chest computed tomography (CT) manifestation of 2019 novel coronavirus (COVID-19) can be differentiated by a radiologist or a computer-based CT image analysis system.MethodsWe conducted a retrospective case-control study that included 52 laboratory-confirmed COVID-19 patients and 80 non-COVID-19 viral pneumonia patients between 20 December, 2019 and 10 February, 2020. The chest CT images were evaluated by radiologists in a double blind fashion. A computer-based image analysis system (uAI system, Lianying Inc., Shanghai, China) detected the lesions in 18 lung segments defined by Boyden classification system and calculated the infected volume in each segment. The number and volume of lesions detected by radiologist and computer system was compared with Chi-square test or Mann-Whitney U test as appropriate.ResultsThe main CT manifestations of COVID-19 were multi-lobar/segmental peripheral ground-glass opacities and patchy air space infiltrates. The case and control groups were similar in demographics, comorbidity, and clinical manifestations. There was no significant difference in eight radiologist identified CT image features between the two groups of patients. There was also no difference in the absolute and relative volume of infected regions in each lung segment.ConclusionsWe documented the non-differentiating nature of initial chest CT image between COVID-19 and other viral pneumonia with suspected symptoms. Our results do not support CT findings replacing microbiological diagnosis as a critical criterion for COVID-19 diagnosis. Our findings may prompt re-evaluation of isolated patients without laboratory confirmation.

A novel approach of lung segmentation on chest CT images using graph cuts

2015 ◽  
Vol 168 ◽  
pp. 799-807 ◽  
Author(s):  
Shuangfeng Dai ◽  
Ke Lu ◽  
Jiyang Dong ◽  
Yifei Zhang ◽  
Yong Chen
Keyword(s):  
Graph Cuts ◽  
Ct Images ◽  
Chest Ct ◽  
Lung Segmentation ◽  
Novel Approach

scholarly journals Diagnosing sarcopenia and myosteatosis based on chest computed tomography images in healthy Chinese adults

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Lingling Tan ◽  
Guiyi Ji ◽  
Ting Bao ◽  
Hongbo Fu ◽  
Ling Yang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Skeletal Muscle ◽  
Muscle Mass ◽  
Ct Images ◽  
Chest Ct ◽  
Muscle Quality ◽  
Whole Body ◽  
Chinese Adults ◽  
Chest Computed Tomography ◽  
Healthy Chinese

Abstract Background Measuring muscle mass and muscle quality based on chest Computed Tomography (CT) images would facilitate sarcopenia and myosteatosis research. We aimed (1) to measure muscle mass and myosteatosis based on chest CT images at the 12th thoracic vertebra level and compare the relevant indicators with whole-body skeletal muscle mass (BSM) and whole-body fat mass (BFM) measured by bioelectrical impedance analysis; and (2) to determine the cut-off points of these indicators for diagnosing sarcopenia or myosteatosis in healthy Chinese adults. Methods Chest CT images were analyzed using a segmentation software. Skeletal muscle area (SMA), skeletal muscle radiodensity (SMD), and intermuscular adiposity tissue (IMAT) were measured. Skeletal muscle indices (SMIs) and IMAT/SMA ratio were calculated. Results We included 569 participants. SMA, SMA/height2, and SMA/BMI were strongly and positively correlated with BSM (r = 0.90, 0.72, and 0.69, respectively, all p < 0.001); whereas SMA/weight was moderately and positively correlated with BSM (r = 0.38, p < 0.001). IMAT and IMAT/SMA were strongly and positively correlated with BFM (r = 0.67 and 0.58, respectively, both p < 0.001). SMD was moderately and negatively correlated with BFM (r = − 0.40, p < 0.001). We suggest SMA/height2 (< 25.75 cm2/m2 in men and < 20.16 cm2/m2 in women) for diagnosing sarcopenia and SMD (< 37.42 HU in men and < 33.17 HU in women) or IMAT (> 8.72 cm2 in men and > 4.58 cm2 in women) for diagnosing myosteatosis. Conclusions Muscle mass indicators (SMA and SMIs) and muscle quality indicators (SMD, IMAT, and IMAT/SMA) measured by chest CT images are valuable for diagnosing sarcopenia and myosteatosis, respectively.

Sign in / Sign up

Export Citation Format

Share Document