Automatic segmentation of ground-glass opacity nodule on chest CT images by histogram modeling and local contrast

2012 ◽  
Author(s):  
Julip Jung ◽  
Helen Hong ◽  
Jin Mo Goo
Keyword(s):  
Automatic Segmentation ◽  
Ground Glass Opacity ◽  
Ct Images ◽  
Chest Ct ◽  
Ground Glass ◽  
Local Contrast

GROUND-GLASS OPACITY DETECTION BY USING CORRELATION BETWEEN SUCCESSIVE SLICE IMAGES

2007 ◽  
Vol 16 (04) ◽  
pp. 583-592 ◽  
Author(s):  
HYOUNGSEOP KIM ◽  
MASAKI MAEKADO ◽  
JOO KOOI TAN ◽  
SEIJI ISHIKAWA ◽  
MASAAKI TSUKUDA
Keyword(s):  
Soft Tissues ◽  
Automatic Segmentation ◽  
Ground Glass Opacity ◽  
Processing Technique ◽  
Ct Images ◽  
Image Processing Technique ◽  
Ground Glass ◽  
Lung Region ◽  
Mdct Image ◽  
Thorax Ct

Medical imaging systems such as computed tomography, magnetic resonance imaging provided a high resolution image for powerful diagnostic tool in visual inspection fields by physician. Especially MDCT image can be used to obtain detailed images of the pulmonary anatomy, including pulmonary diseases such as the pulmonary nodules, the pulmonary vein, etc. In the medical image processing technique, segmentation is a difficult task because surrounding soft tissues and organs have similar CT values and sometimes contact with each other. We propose a new technique for automatic segmentation of lung regions and its classification for ground-glass opacity from the extracted lung regions by computer based on a set of the thorax CT images. In this paper, we segment the lung region for extraction of the region of interest employing binarization and labeling process from the inputted each slices images. The region having the largest area is regarded as the tentative lung regions. Furthermore, the ground-glass opacity is classified by correlation distribution on the slice to slice from the extracted lung region with respect to the thorax CT images. Experiment is performed employing twenty six thorax CT image sets and 96% of recognition rates were achieved. Obtained results are shown along with a discussion.

scholarly journals Development a quantitative segmentation model to assess the effect of comorbidity on patients with COVID-19

2020 ◽  
Author(s):  
Cui Zhang ◽  
Guangzhao Yang ◽  
Chunxian Cai ◽  
Zhihua Xu ◽  
Hai Wu ◽  
...  
Keyword(s):  
Automatic Segmentation ◽  
Ground Glass Opacity ◽  
Ct Images ◽  
Community Acquired Pneumonia ◽  
Ground Glass ◽  
Quantitative Ct ◽  
Laboratory Findings ◽  
Diabetes Mellitus Group ◽  
Test Dataset ◽  
Radiological Evidence

Abstract Background: The coronavirus disease 2019 (COVID-19) has brought a global disaster. Quantitative lesions may provide the radiological evidence of the severity of pneumonia and further to assess the effect of comorbidity on patients with COVID-19.Methods: 294 patients with COVID-19 were enrolled from February, 24, 2020 to June, 1, 2020 from six centers. Multi-task Unet network was used to segment the whole lung and lesions from chest CT images. This deep learning method was pre-trained in 650 CT images (550 in primary dataset and 100 in test dataset) with COVID-19 or community acquired pneumonia and Dice coefficients in test dataset were calculated. 50 CT scans of 50 patients (15 with comorbidity and 35 without comorbidity) were random selected to mark lesions manually. The results will be compared with the automatic segmentation model. Eight quantitative parameters were calculated based on the segmentation results to evaluate the effect of comorbidity on patients with COVID-19.Results: Quantitative segmentation model was proved to be effective and accurate with all Dice coefficients more than 0.85 and all accuracies more than 0.95. Of the 294 patients, 52 (17.7%) patients were reported having at least one comorbidity, 14 (4.8%) having more than one comorbidity. Patients with any comorbidity were older (P<0.001), had longer incubation period (P<0.001), were more likely to have abnormal laboratory findings (P<0.05) and be in severity status (P<0.001). More lesions (including larger volume of lesion, consolidation and ground-glass opacity) were shown in patients with any comorbidity than patients without comorbidity (all P<0.001). The more comorbidities patients have, the poorer CT manifestation is. The median volume of lesion, consolidation and ground-glass opacity in diabetes mellitus group was largest among the three prevalently single comorbidity groups.Conclusions: Multi-task Unet network can make quantitative CT analysis of lesions to assess the effect of comorbidity on patients with COVID-19, further to provide the radiological evidence of the severity of pneumonia. More lesions were found in CT images of cases with comorbidity. The more comorbidities patients have, the poorer CT manifestation is.

scholarly journals Development a quantitative segmentation model to assess the effect of comorbidity on patients with COVID-19

2020 ◽  
Author(s):  
Cui Zhang ◽  
Guangzhao Yang ◽  
Chunxian Cai ◽  
Zhihua Xu ◽  
Hai Wu ◽  
...  
Keyword(s):  
Automatic Segmentation ◽  
Ground Glass Opacity ◽  
Ct Images ◽  
Community Acquired Pneumonia ◽  
Ground Glass ◽  
Quantitative Ct ◽  
Laboratory Findings ◽  
Diabetes Mellitus Group ◽  
Test Dataset ◽  
Radiological Evidence

Abstract Background: The coronavirus disease 2019 (COVID-19) has brought a global disaster. Quantitative lesions may provide the radiological evidence of the severity of pneumonia and further to assess the effect of comorbidity on patients with COVID-19.Methods: 294 patients with COVID-19 were enrolled from February, 24, 2020 to June, 1, 2020 from six centers. Multi-task Unet network was used to segment the whole lung and lesions from chest CT images. This deep learning method was pre-trained in 650 CT images (550 in primary dataset and 100 in test dataset) with COVID-19 or community acquired pneumonia and Dice coefficients in test dataset were calculated. 50 CT scans of 50 patients (15 with comorbidity and 35 without comorbidity) were random selected to mark lesions manually. The results will be compared with the automatic segmentation model. Eight quantitative parameters were calculated based on the segmentation results to evaluate the effect of comorbidity on patients with COVID-19.Results: Quantitative segmentation model was proved to be effective and accurate with all Dice coefficients more than 0.85 and all accuracies more than 0.95. Of the 294 patients, 52 (17.7%) patients were reported having at least one comorbidity, 14 (4.8%) having more than one comorbidity. Patients with any comorbidity were older (P<0.001), had longer incubation period (P<0.001), were more likely to have abnormal laboratory findings (P<0.05) and be in severity status (P<0.001). More lesions (including larger volume of lesion, consolidation and ground-glass opacity) were shown in patients with any comorbidity than patients without comorbidity (all P<0.001). More lesions were found on CT images in patients with more comorbidities. The median volumes of lesion, consolidation and ground-glass opacity in diabetes mellitus group were largest among the groups with single comorbidity that had the incidence rate of top three.Conclusions: Multi-task Unet network can make quantitative CT analysis of lesions to assess the effect of comorbidity on patients with COVID-19, further to provide the radiological evidence of the severity of pneumonia. More lesions (including GGO and consolidation) were found in CT images of cases with comorbidity. The more comorbidities patients have, the more lesions CT images show.

scholarly journals Spectrum of Chest Computerised Tomography (CT) findings of Covid 19 in Indian Population

2021 ◽  
Vol 9 (1) ◽  
pp. 43-49
Author(s):  
Shravya Boini ◽  
Vikas Chennamaneni ◽  
Vamshi Kiran Diddy ◽  
Momin Sayed Kashif
Keyword(s):  
Lower Lobe ◽  
Ground Glass Opacity ◽  
Ct Images ◽  
Chest Ct ◽  
Ct Scans ◽  
Ground Glass ◽  
Study Cohort ◽  
Study Population ◽  
Ct Features

Background: To analyze the chest computed tomography (CT) features in patients with coronavirus disease 2019 (COVID-19) pneumonia. Methods: This was a prospective descriptive study comprising 202 consecutive reverse transcriptase polymerase chain reaction (RT-PCR) positive patients who underwent CT chest. For 25 patients, follow-up CT scans were obtained. The CT images were evaluated for the number, type and distribution of the opacity, and CT severity scoring was done Results: Among the total study cohort of 202 patients, 152 were males and 50 were females .From July 07, 2020, to september07, 2020, totally 202 laboratory-confirmed patients with COVID-19 underwent chest CT. For 25 patients, follow-up CT scans were obtained. The CT images were evaluated for the number, type and distribution of the opacity, and the affected lung lobes. Furthermore, the initial CT scan and the follow-up CT scans were compared. Results were patients (98.5%) had two or more opacities in the lung and 3 (1.5%) patients has negative chest CT. 183 (90.6%) patients had only ground-glass opacities; 13 patients (6.4%) had ground-glass and consolidative opacities; and 3 patients (1.5%) had only consolidation. A total 192 of patients (96.5%) showed two or more lobes involved. The opacities tended to be both in peripheral and central 7 (3.5%) or purely peripheral distribution 192 (96.5%). 177 patients (88.9%) had the lower lobe involved.8 patients showed complete resolution of lung findings. Conclusion: In this study population, the typical CT features of COVID 19 pneumonia are ground glass opacity with or without consolidation, which is patchy and peripheral, predominantly in lower lobes.

scholarly journals Development of a quantitative segmentation model to assess the effect of comorbidity on patients with COVID-19

2020 ◽  
Vol 25 (1) ◽  
Author(s):  
Cui Zhang ◽  
Guangzhao Yang ◽  
Chunxian Cai ◽  
Zhihua Xu ◽  
Hai Wu ◽  
...  
Keyword(s):  
Automatic Segmentation ◽  
Ground Glass Opacity ◽  
Ct Images ◽  
Community Acquired Pneumonia ◽  
Ground Glass ◽  
Quantitative Ct ◽  
Laboratory Findings ◽  
Diabetes Mellitus Group ◽  
Test Dataset ◽  
Radiological Evidence

Abstract Background The coronavirus disease 2019 (COVID-19) has brought a global disaster. Quantitative lesions may provide the radiological evidence of the severity of pneumonia and further to assess the effect of comorbidity on patients with COVID-19. Methods 294 patients with COVID-19 were enrolled from February, 24, 2020 to June, 1, 2020 from six centers. Multi-task Unet network was used to segment the whole lung and lesions from chest CT images. This deep learning method was pre-trained in 650 CT images (550 in primary dataset and 100 in test dataset) with COVID-19 or community-acquired pneumonia and Dice coefficients in test dataset were calculated. 50 CT scans of 50 patients (15 with comorbidity and 35 without comorbidity) were random selected to mark lesions manually. The results will be compared with the automatic segmentation model. Eight quantitative parameters were calculated based on the segmentation results to evaluate the effect of comorbidity on patients with COVID-19. Results Quantitative segmentation model was proved to be effective and accurate with all Dice coefficients more than 0.85 and all accuracies more than 0.95. Of the 294 patients, 52 (17.7%) patients were reported having at least one comorbidity; 14 (4.8%) having more than one comorbidity. Patients with any comorbidity were older (P < 0.001), had longer incubation period (P < 0.001), were more likely to have abnormal laboratory findings (P < 0.05), and be in severity status (P < 0.001). More lesions (including larger volume of lesion, consolidation, and ground-glass opacity) were shown in patients with any comorbidity than patients without comorbidity (all P < 0.001). More lesions were found on CT images in patients with more comorbidities. The median volumes of lesion, consolidation, and ground-glass opacity in diabetes mellitus group were largest among the groups with single comorbidity that had the incidence rate of top three. Conclusions Multi-task Unet network can make quantitative CT analysis of lesions to assess the effect of comorbidity on patients with COVID-19, further to provide the radiological evidence of the severity of pneumonia. More lesions (including GGO and consolidation) were found in CT images of cases with comorbidity. The more comorbidities patients have, the more lesions CT images show.

scholarly journals Diagnostic performance of chest CT in differentiating COVID-19 from other causes of ground-glass opacities

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Ali H. Elmokadem ◽  
Dalia Bayoumi ◽  
Sherif A. Abo-Hedibah ◽  
Ahmed El-Morsy
Keyword(s):  
Diagnostic Performance ◽  
Pulmonary Nodules ◽  
Ground Glass Opacity ◽  
Chest Ct ◽  
Ground Glass ◽  
Rt Pcr ◽  
Laboratory Findings ◽  
Halo Sign ◽  
Traction Bronchiectasis ◽  
Chest Ct Scan

Abstract Background To evaluate the diagnostic performance of chest CT in differentiating coronavirus disease 2019 (COVID-19) and non-COVID-19 causes of ground-glass opacities (GGO). Results A total of 80 patients (49 males and 31 females, 46.48 ± 16.09 years) confirmed with COVID-19 by RT-PCR and who underwent chest CT scan within 2 weeks of symptoms, and 100 patients (55 males and 45 females, 48.94 ± 18.97 years) presented with GGO on chest CT were enrolled in the study. Three radiologists reviewed all CT chest exams after removal of all identifying data from the images. They expressed the result as positive or negative for COVID-19 and recorded the other pulmonary CT features with mention of laterality, lobar affection, and distribution pattern. The clinical data and laboratory findings were recorded. Chest CT offered diagnostic accuracy ranging from 59 to 77.2% in differentiating COVID-19- from non-COVID-19-associated GGO with sensitivity from 76.25 to 90% and specificity from 45 to 67%. The specificity was lower when differentiating COVID-19 from non-COVID-19 viral pneumonias (30.5–61.1%) and higher (53.1–70.3%) after exclusion of viral pneumonia from the non-COVID-19 group. Patients with COVID-19 were more likely to have lesions in lower lobes (p = 0.005), peripheral distribution (p < 0.001), isolated ground-glass opacity (p = 0.043), subpleural bands (p = 0.048), reverse halo sign (p = 0.005), and vascular thickening (p = 0.013) but less likely to have pulmonary nodules (p < 0.001), traction bronchiectasis (p = 0.005), pleural effusion (p < 0.001), and lymphadenopathy (p < 0.001). Conclusions Chest CT offered reasonable sensitivity when differentiating COVID-19- from non-COVID-19-associated GGO with low specificity when differentiating COVID-19 from other viral pneumonias and moderate specificity when differentiating COVID-19 from other causes of GGO.

Study on the Dynamic Changes in Chest CT Findings of COVID-19 Rehabilitated Patients

2020 ◽  
pp. 1-7
Keyword(s):  
Disease Progression ◽  
Onset Time ◽  
Ground Glass Opacity ◽  
Chest Ct ◽  
Ground Glass ◽  
Dynamic Changes ◽  
Course Of Disease ◽  
Pleural Thickening ◽  
Ct Findings ◽  
Interlobular Septal Thickening

Objective: To study the dynamic changes in CT findings in COVID-19 (coronavirus disease-19, COVID-19) rehabilitated patients. Methods: A total of 148 chest CT images of 37 patients with COVID-19 were collected. In the first 21 days of the course of disease, 7 stages were performed every 3 days, and the eighth stage was performed after 21 days. Results: In the first chest CT examination, 19 cases were ground glass opacity, and 18 cases were high-density shadows with consolidation. The lesion shape was flaky and patchy in 33 cases. The percentage of consolidation, air bronchogram, fiber cord, interlobular septal thickening, subpleural line and pleural thickening were the highest on days 4-6, 7-9, 7-9, 10-12, 19-21 and 19-21, respectively. The highest percentage of disease progression was 80.00% on days 4-6, and then the percentage of disease progression gradually decreased with the extension of the onset time. The percentage of patients with improvement gradually increased from days 4-6, reaching 83.33% on days 16-18 and 100.00% on day 21. The percentage of lesion range enlargement and density increase was the highest on days 4-6, both of which were 60.00%，Then the percentage of both decreased gradually. The percentage of patients with lesion range reduction and density absorption dilution increased gradually with the onset time. There was no obvious regularity in the number of lesions. Conclusion: Patients with COVID-19 have regular changes in their lung conditions.

Pure ground glass opacity (GGO) on chest CT: a rare presentation of lung metastasis of Malignant Phyllodes Tumor

Breast Cancer ◽  
2020 ◽  
Vol 27 (6) ◽  
pp. 1187-1190
Author(s):  
Shoko Nakamura ◽  
Takeshi Goto ◽  
Satoshi Nara ◽  
Yoichiro Kawahara ◽  
Shinichi Yashiro ◽  
...  
Keyword(s):  
Lung Metastasis ◽  
Ground Glass Opacity ◽  
Phyllodes Tumor ◽  
Chest Ct ◽  
Ground Glass ◽  
Rare Presentation ◽  
Malignant Phyllodes Tumor

scholarly journals Analysis of Chest CT Results of Coronavirus Disease 2019 (COVID-19) Patients at First Follow-Up

2020 ◽  
Vol 2020 ◽  
pp. 1-8 ◽  
Author(s):  
Lingshan Zhong ◽  
Shuo Zhang ◽  
Jigang Wang ◽  
Xinqian Zhao ◽  
Kai Wang ◽  
...  
Keyword(s):  
Ground Glass Opacity ◽  
Chest Ct ◽  
Ground Glass ◽  
Imaging Data ◽  
Exclusion Criteria ◽  
Pulmonary Lesions ◽  
Normal Chest ◽  
Time Of Admission ◽  
Better Than

Objective. To investigate the dissipation and outcomes of pulmonary lesions at the first follow-up of patients who recovered from moderate and severe cases of COVID-19. Methods. From January 21 to March 3, 2020, a total of 136 patients with COVID-19 were admitted to our hospital. According to inclusion and exclusion criteria, 52 patients who recovered from COVID-19 were included in this study, including 33 moderate cases and 19 severe cases. Three senior radiologists independently and retrospectively analyzed the chest CT imaging data of 52 patients at the last time of admission and the first follow-up after discharge, including primary manifestations, concomitant manifestations, and degree of residual lesion dissipation. Results. At the first follow-up after discharge, 16 patients with COVID-19 recovered to normal chest CT appearance, while 36 patients still had residual pulmonary lesions, mainly including 33 cases of ground-glass opacity, 5 cases of consolidation, and 19 cases of fibrous strip shadow. The proportion of residual pulmonary lesions in severe cases (17/19) was statistically higher than in moderate cases (19/33) ( χ 2   =   5 . 759 , P < 0.05 ). At the first follow-up, residual pulmonary lesions were dissipated to varying degrees in 47 cases, and lesions remained unchanged in 5 cases. There were no cases of increased numbers of lesions, enlargement of lesions, or appearance of new lesions. The dissipation of residual pulmonary lesions in moderate patients was statistically better than in severe patients (Z = −2.538, P < 0.05 ). Conclusion. Clinically cured patients with COVID-19 had faster dissipation of residual pulmonary lesions after discharge, while moderate patients had better dissipation than severe patients. However, at the first follow-up, most patients still had residual pulmonary lesions, which were primarily ground-glass opacity and fibrous strip shadow. The proportion of residual pulmonary lesions was higher in severe cases of COVID-19, which required further follow-up.

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