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 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 Common and Uncommon Imaging Appearances of COVID-19 Pneumonia in Young and Middle-Aged Group and Elderly Group

2021 ◽  
Author(s):  
Alireza Mahmoudabadi ◽  
Hamed Masoumi ◽  
Mohammad Keshtkar ◽  
Arash Azhideh ◽  
Hamidreza Haghighatkhah
Keyword(s):  
Pleural Effusion ◽  
Ground Glass Opacity ◽  
The Elderly ◽  
Ct Images ◽  
Elderly Group ◽  
Wall Thickening ◽  
Lung Region ◽  
Middle Aged ◽  
Aged Group ◽  
Lower Lung

Purpose: In this study, we retrospectively evaluated chest Computed Tomography (CT) imaging manifestations of the patients with Coronavirus Disease 2019 (COVID-19) to simplify prompt early diagnosis of disease and speed up needed actions for infected patients. Materials and Methods: Totally, 75 patients who laboratory confirmed COVID-19 pneumonia were enrolled in this study. CT images, demographic and some clinical data of all patients were collected and analyzed retrospectively. Furthermore, for comparison, the patients were divided into two groups as follows: the young and middle-aged group (< 60 years old) and the elderly group (≥ 60 years old). Results: Based on the evaluation of CT images, 33 patients (44%) showed Ground-Glass Opacity (GGO), 15 patients (20%) showed consolidation, 24 patients (32%) showed mixed GGO and consolidation, 2 patients (2.6%) had bronchial wall thickening, 10 patients (13.3%) had a crazy paving sign, 35 patients (46.6%) had air bronchogram and, 7 patients (9.3%) had cavitation and 2 patients (2.6%) had a tree in the bud. CT images of 3 patients (4%) were normal. In terms of out of lung changes, lymphadenopathy was observed in one patient (1.3%), pleural effusion in 12 patients (16%), and pericardial effusion in 2 patients (2.6%). Lesions were found predominantly in the peripheral (57.3%) and the lower lung region (60%). Conclusion: CT images of the COVID-19 patients showed various aspects, mainly GGO, consolidation, mixed GGO and consolidation, and air bronchogram. Lesion distribution was predominantly in lower lung region, bilateral and peripheral. Pleural effusion and multiple lobe involvement were significantly higher in the elderly group than that of the young and middle-aged group.

An image processing technique for the identification of contact pixels applied to X-ray CT images of implanted hip prostheses

1993 ◽  
Vol 38 (2) ◽  
pp. 323-328 ◽  
Author(s):  
E Berry ◽  
V G Langkamer ◽  
P C Jackson ◽  
M Snow ◽  
P R Goddard ◽  
...  
Keyword(s):  
Image Processing ◽  
Processing Technique ◽  
Ct Images ◽  
Image Processing Technique ◽  
Hip Prostheses ◽  
X Ray

scholarly journals Studi Awal Deteksi COVID-19 Menggunakan Citra CT Berbasis Deep Learning

2020 ◽  
Vol 7 (3) ◽  
pp. 629
Author(s):  
Windra Swastika
Keyword(s):  
Deep Learning ◽  
Image Data ◽  
Ground Glass Opacity ◽  
Normal Subjects ◽  
Ct Images ◽  
Ground Glass ◽  
Virus Spread ◽  
Training Process ◽  
Specificity And Sensitivity ◽  
Preliminary Study

<p class="Abstrak">Pada bulan Desember 2019, virus COVID-19 menyebar ke banyak negara, termasuk di Indonesia yang kemudian menjadi pandemi dan menimbulkan masalah serius karena masih belum adanya vaksin untuk mencegah penularan. Uji spesimen saluran nafas atas dan saluran nafas bawah saat ini merupakan salah satu metode yang efektif untuk mengetahui apakah seseorang terinfeksi COVID-19 atau tidak. Salah satu indikasi dari infeksi COVID-19 adalah sesak nafas atau pneumonia serta munculnya <em>ground-glass opacity</em> pada citra CT. Penelitian ini merupakan studi awal untuk melihat apakah citra CT dari organ thorax dapat digunakan sebagai alternatif untuk mendeteksi infeksi virus COVID-19. Deep learning digunakan untuk membuat sebuah model dengan citra CT sebagai masukan. Total 140 data citra CT yang terbagi menjadi 2 yaitu citra dari pasien terinfeksi dan citra dari subjek normal digunakan sebagai masukan pada deep learning. Proses pelatihan dilakukan menggunakan CNN dengan arsitektur VGG16 dan optimizer SGD dan Adam. Hasil yang didapatkan adalah akurasi sebesar 92,86% untuk mengklasifikasikan infeksi COVID-19 dan normal. Nilai spesifisitas dan sensitivitas sebesar 100% dan 85,71% untuk pelatihan dengan menggunakan optimizer SGD.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>In December 2019, the COVID-19 virus spread to many countries, including Indonesia which later became a pandemic and caused serious problems because there was still no vaccine to prevent transmission. Tests of upper and lower respiratory tract specimens are now an effective method of finding whether a person is infected with COVID-19 or not. One indication of COVID-19 infection is shortness of breath or pneumonia and the appearance of ground-glass opacity on CT images. This research is a preliminary study to see whether CT images of the thorax organs can be used as an alternative to detect COVID-19 virus. The deep learning is used to create a model with CT images as input. A total of 140 CT image data which are divided into 2 images from infected patients and images from normal subjects are used as input for deep learning. The training process is carried out using CNN with VGG16 architecture and SGD and Adam optimizers. The results obtained are 92.86% accuracy for classifying COVID-19 infections and normal. Specificity and sensitivity values were 100% and 85.71% for training using the SGD optimizer.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

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