TU-H-CAMPUS-JeP2-03: Machine-Learning-Based Delineation Framework of GTV Regions of Solid and Ground Glass Opacity Lung Tumors at Datasets of Planning CT and PET/CT Images

2016 ◽  
Vol 43 (6Part37) ◽  
pp. 3782-3782
Author(s):  
K Ikushima ◽  
H Arimura ◽  
Z Jin ◽  
H Yabuuchi ◽  
J Kuwazuru ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ground Glass Opacity ◽  
Lung Tumors ◽  
Ct Images ◽  
Ground Glass ◽  
Pet Ct ◽  
Planning Ct

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 Pathologic and Biological Assessment of Lung Tumors Showing Ground-Glass Opacity

2006 ◽  
Vol 81 (4) ◽  
pp. 1194-1197 ◽  
Author(s):  
Yasuhiko Ohta ◽  
Yosuke Shimizu ◽  
Takeshi Kobayashi ◽  
Osamu Matsui ◽  
Hiroshi Minato ◽  
...  
Keyword(s):  
Ground Glass Opacity ◽  
Lung Tumors ◽  
Biological Assessment ◽  
Ground Glass

18F-FDG and 68Ga-FAPI PET/CT in the Evaluation of Ground-Glass Opacity Nodule

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Haojun Chen ◽  
Yizhen Pang ◽  
Tinghua Meng ◽  
Xiuyi Yu ◽  
Long Sun
Keyword(s):  
Ground Glass Opacity ◽  
Ground Glass ◽  
Pet Ct ◽  
18F Fdg

Lung Adenocarcinoma Manifesting as Ground-Glass Opacity Nodules 3 cm or Smaller: Evaluation With Combined High-Resolution CT and PET/CT Modality

2019 ◽  
Vol 213 (5) ◽  
pp. W236-W245 ◽  
Author(s):  
Rong Niu ◽  
Xiaonan Shao ◽  
Xiaoliang Shao ◽  
Jianfeng Wang ◽  
Zhenxing Jiang ◽  
...  
Keyword(s):  
High Resolution ◽  
Lung Adenocarcinoma ◽  
Ground Glass Opacity ◽  
Ground Glass ◽  
High Resolution Ct ◽  
Pet Ct

A prediction model for pathological findings after neoadjuvant chemoradiotherapy for resectable locally advanced esophageal cancer based on PET images using radiomics and machine-learning.

2020 ◽  
Vol 38 (4_suppl) ◽  
pp. 456-456
Author(s):  
Yuji Murakami ◽  
Yasushi Nagata ◽  
Daisuke Kawahara
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Predictive Model ◽  
Test Data ◽  
Locally Advanced ◽  
Neoadjuvant Chemoradiotherapy ◽  
Image Data ◽  
Ct Images ◽  
Pathological Findings ◽  
Planning Ct

456 Background: The pathologic complete response (PCR) rate by neoadjuvant chemoradiotherapy (NCRT) for resectable locally advanced esophageal squamous cell carcinoma (ESCC) is about 40%. If we could predict a PCR from pre-treatment image data, it might be possible to select patients who can be cured by organ-preserving CRT. The purpose of this study is to construct a predictive model for PCR by NCRT in patients with locally advanced ESCC using radiomics and machine-learning. Methods: We used data of 98 ESCC patients who underwent NCRT and surgery from 2003 to 2016. Firstly, we fused the radiotherapy treatment planning CT images and PET images scanned before treatment. Then using target delineations on planning CT images, we created eight kinds of target regions on PET images. Secondly, we generated a total of 6968 features per patient using the PET image data within these target regions that were preprocessed by radiomics technique. Among them, we extracted the optimal features for machine-learning using the least absolute shrinkage and selection operator (LASSO) logistic regression. Thirdly, artificial neural networks were used as a machine-learning method to create a predictive model. The extracted radiomics features were used as input values, and the information of ‘PCR’ or ‘not PCR’ was used as output values. We used data of randomly selected 58 patients for training and constructed a predictive model. Then we used data of 15 patients to validate the models and created the optimal model. Finally, we evaluated the predictive model using the test data of 25 patients. Results: By the LASSO analysis, 32 radiomics features were extracted for machine-learning classification. This predictive model predicted pathological findings after NCRT in 24 of 25 test data. The accuracy, specificity and sensitivity in the prediction of PCR after NCRT by this predictive model were 96.0%, 93.8%, and 100%, respectively. Conclusions: A prediction model based on PET images using radiomics and machine-learning could predict pathological findings after NCRT for resectable locally advanced ESCC.

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>

scholarly journals Machine learning for differentiating metastatic and completely responded sclerotic bone lesion in prostate cancer: a retrospective radiomics study

2019 ◽  
Vol 92 (1101) ◽  
pp. 20190286 ◽  
Author(s):  
Emine Acar ◽  
Asım Leblebici ◽  
Berat Ender Ellidokuz ◽  
Yasemin Başbınar ◽  
Gamze Çapa Kaya
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Texture Analysis ◽  
Area Under The Curve ◽  
Ct Images ◽  
Metastatic Lesions ◽  
Psma Pet ◽  
Pet Ct ◽  
Sclerotic Lesions ◽  
Psma Expression

Objective:Using CT texture analysis and machine learning methods, this study aims to distinguish the lesions imaged via 68Ga-prostate-specific membrane antigen (PSMA) positron emission tomography (PET)/CT as metastatic and completely responded in patients with known bone metastasis and who were previously treated.Methods:We retrospectively reviewed the 68Ga-PSMA PET/CT images of 75 patients after treatment, who were previously diagnosed with prostate cancer and had known bone metastasis. A texture analysis was performed on the metastatic lesions showing PSMA expression and completely responded sclerotic lesions without PSMA expression through CT images. Textural features were compared in two groups. Thus, the distinction of metastasis/completely responded lesions and the most effective parameters in this issue were determined by using various methods [decision tree, discriminant analysis, support vector machine (SVM), k-nearest neighbor (KNN), ensemble classifier] in machine learning.Results:In 28 of the 35 texture analysis findings, there was a statistically significant difference between the two groups. The Weighted KNN method had the highest accuracy and area under the curve, has been chosen as the best model. The weighted KNN algorithm was succeeded to differentiate sclerotic lesion from metastasis or completely responded lesions with 0.76 area under the curve. GLZLM_SZHGE and histogram-based kurtosis were found to be the most important parameters in differentiating metastatic and completely responded sclerotic lesions.Conclusions:Metastatic lesions and completely responded sclerosis areas in CT images, as determined by 68Ga-PSMA PET, could be distinguished with good accuracy using texture analysis and machine learning (Weighted KNN algorithm) in prostate cancer.Advances in knowledge:Our findings suggest that, with the use of newly emerging software, CT imaging can contribute to identifying the metastatic lesions in prostate cancer.

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.

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