A novel localization technique for peripheral ground glass opacity using geometric parameters measured on CT images

Abstract Background Currently no optimal localization technique has been established for localization of ground glass opacity (GGO). We aimed to introduce a localization technique using geometric localization for peripheral GGO. Methods We delineated the location of pulmonary GGO using geometric method which was similar with localization of a point in a spatial coordinate system. The localization technique was based on the anatomical landmarkers (ribs or intercostal spaces, capitulum costae and sternocostal joints). The geometric parameters were measured on preoperative CT images and the targeted GGO could be identified intraoperatively according to the parameters. We retrospectively collected the data of the patients with peripheral GGOs which were localized using this method and were wedge resected between June 2019 and July 2020. The efficacy and feasibility of the localization technique were assessed. Results There were 93 patients (male 34, median = 55 years) with 108 peripheral GGOs in the study. All the targeted GGOs were successfully wedge resected in the operative field with negative surgical margin at the first attempt. For each GGO, the localization parameters could be measured in 2–4 min (median = 3 min) on CT images before operation, and surgical resection could be completed in 5–10 min (median = 7 min). A total of 106 (98.15%) GGOs achieved sufficient resection margin. No complications and deaths occurred related to the localization and surgical procedure. Conclusions The localization technique can achieve satisfactory localization success rate and good safety profile. It can provide an easy-to-use alternative to localize peripheral GGO.

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

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213007003448 ◽

2007 ◽

Vol 16 (04) ◽

pp. 583-592 ◽

Cited By ~ 1

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.

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

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2020733399 ◽

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

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 ground-glass opacity 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. AbstractIn 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.

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Delayed cut-end recurrence after wedge resection for pulmonary ground-glass opacity adenocarcinoma despite negative surgical margin

General Thoracic and Cardiovascular Surgery ◽

10.1007/s11748-019-01176-6 ◽

2019 ◽

Vol 68 (6) ◽

pp. 644-648

Author(s):

Keigo Sekihara ◽

Junji Yoshida ◽

Makoto Oda ◽

Tomonari Oki ◽

Takuya Ueda ◽

...

Keyword(s):

Wedge Resection ◽

Surgical Margin ◽

Ground Glass Opacity ◽

Ground Glass

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Efficient Computer-Aided Detection of Ground-Glass Opacity Nodules in Thoracic CT Images

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society ◽

10.1109/iembs.2007.4353326 ◽

2007 ◽

Cited By ~ 10

Author(s):

Xujiong Ye ◽

Xinyu Lin ◽

Gareth Beddoe ◽

Jamshid Dehmeshki

Keyword(s):

Ground Glass Opacity ◽

Ct Images ◽

Ground Glass ◽

Computer Aided Detection ◽

Thoracic Ct ◽

Computer Aided

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A New CAD System for Detecting Localized Ground Glass Opacity Nodules in Lung CT images Using Cross and Coronary Section Images

IEEE International Workshop on Medical Measurement and Applications, 2006. MeMea 2006. ◽

10.1109/memea.2006.1644460 ◽

2006 ◽

Cited By ~ 2

Author(s):

H.A. Bastawrous ◽

N. Nitta ◽

M. Tsudagawa

Keyword(s):

Ground Glass Opacity ◽

Ct Images ◽

Ground Glass ◽

Cad System ◽

Lung Ct

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Early clinical and CT features of COVID-19 and community-acquired pneumonia from a fever observation ward in Ningbo, China

Singapore Medical Journal ◽

10.11622/smedj.2021004 ◽

2021 ◽

Author(s):

G Qian ◽

Y Lin ◽

AHY Ma ◽

X Zhang ◽

G Li ◽

...

Keyword(s):

Early Stage ◽

Laboratory Data ◽

Clinical Manifestations ◽

Lower Lobe ◽

Ground Glass Opacity ◽

Ct Images ◽

Community Acquired Pneumonia ◽

Ground Glass ◽

Significant Difference ◽

Eosinophil Counts

Introduction: We aimed to compare the early clinical manifestations, laboratory results and chest computed tomography (CT) images of coronavirus disease 2019 (COVID-19) patients with those of other community-acquired pneumonia (CAP) patients to differentiate COVID-19 before reverse transcription-polymerase chain reaction results are obtained. Methods: The clinical and laboratory data and chest CT images of 51 patients were assessed in a fever observation ward for evidence of COVID-19 between January and February 2020. Results: 24 patients had laboratory-confirmed COVID-19, whereas 27 individuals had negative results. No statistical difference in clinical features was found between COVID-19 and CAP patients except for diarrhoea. There was a significant difference in lymphocyte and eosinophil counts between COVID-19 and CAP patients. 22 (91.67%) COVID-19 patients had bilateral involvement and multiple lesions according to their lung CT images; the left lower lobe (87.50%) and right lower lobe (95.83%) were most often affected, and all lesions were located in peripheral zones of the lung. The most common CT feature of COVID-19 was ground-glass opacity, found in 95.83% of patients, compared to 66.67% of CAP patients. Conclusion: Diarrhoea, lymphocyte counts, eosinophil counts and CT findings (e.g. ground-glass opacity) could help to distinguish COVID-19 from CAP at an early stage of infection, based on findings from our fever observation ward.

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

10.21203/rs.3.rs-52343/v1 ◽

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.

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Lung Adenocarcinoma with Mixed Solid and Crazy-paving Appearances

10.22541/au.162254719.96493355/v1 ◽

2021 ◽

Author(s):

Roqiah Abdul Kadir ◽

Bushra Johari ◽

Mohammad Hanafiah ◽

Lily Zainudin

Keyword(s):

Computed Tomography ◽

Lung Adenocarcinoma ◽

Ground Glass Opacity ◽

Ct Images ◽

Ground Glass ◽

Linear Pattern ◽

Alveolar Proteinosis

‘Crazy-paving’ refers to the superimposition of ground-glass opacity and linear pattern on computed tomography (CT) images. ‘Crazy-paving’ was initially pathognomonic for alveolar proteinosis. Lung adenocarcinoma demonstrating both solid and crazy-paving appearances on CT is a rare occurance.

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

10.21203/rs.3.rs-52343/v2 ◽

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.

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