scholarly journals Strided Self-Supervised Low-Dose CT Denoising for Lung Nodule Classification

Phenomics ◽  
2021 ◽  
Author(s):  
Yiming Lei ◽  
Junping Zhang ◽  
Hongming Shan
Keyword(s):  
Low Dose ◽  
Lung Nodule ◽  
Low Dose Ct ◽  
Nodule Classification

scholarly journals Shape and margin-aware lung nodule classification in low-dose CT images via soft activation mapping

2020 ◽  
Vol 60 ◽  
pp. 101628 ◽  
Author(s):  
Yiming Lei ◽  
Yukun Tian ◽  
Hongming Shan ◽  
Junping Zhang ◽  
Ge Wang ◽  
...  
Keyword(s):  
Low Dose ◽  
Lung Nodule ◽  
Ct Images ◽  
Low Dose Ct ◽  
Nodule Classification ◽  
Activation Mapping

LUNGRADS AND UPDATE OF LUNG NODULES SCREENING BY LOW DOSE COMPUTED TOMOGRAPHY

2015 ◽  
pp. 12-19
Author(s):  
Thi Ngoc Ha Hoang ◽  
Trong Khoan Le
Keyword(s):  
Lung Cancer ◽  
Low Dose ◽  
False Negative ◽  
Lung Nodule ◽  
Lung Cancer Mortality ◽  
Smoking History ◽  
Low Dose Ct ◽  
History Of ◽  
Chest X Ray ◽  
Low Dose Computed Tomography

Background: A pulmonary nodule is defined as a rounded or irregular opacity, well or poorly defined, measuring up to 3 cm in diameter. Early detection the malignancy of nodules has a significant role in decreasing the mortality, increasing the survival time and consider as early diagnosis lung cancer. The main risk factors are those of current or former smokers, aged 55 to 74 years with a smoking history of at least 1 pack-day. Low dose CT: screening individuals with high risk of lung cancer by low dose CT scans could reduce lung cancer mortality by 20 percent compared to chest X-ray. Radiation dose has to maximum reduced but respect the rule of ALARA (As Low as Resonably Archivable). LungRADS 2014: Classification of American College of Radiology, LungRADS, is a newly application but showed many advantages in comparison with others classification such as increasing positive predict value (PPV), no result of false negative and cost effectiveness. Key words: LungRADS, screening lung nodule, low dose CT, lung cancer

Persistent pulmonary subsolid nodules: model-based iterative reconstruction for nodule classification and measurement variability on low-dose CT

2014 ◽  
Vol 24 (11) ◽  
pp. 2700-2708 ◽  
Author(s):  
Hyungjin Kim ◽  
Chang Min Park ◽  
Seong Ho Kim ◽  
Sang Min Lee ◽  
Sang Joon Park ◽  
...  
Keyword(s):  
Iterative Reconstruction ◽  
Low Dose ◽  
Measurement Variability ◽  
Low Dose Ct ◽  
Model Based ◽  
Nodule Classification

scholarly journals 3D CNN with Visual Insights for Early Detection of Lung Cancer Using Gradient-Weighted Class Activation

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Eali Stephen Neal Joshua ◽  
Debnath Bhattacharyya ◽  
Midhun Chakkravarthy ◽  
Yung-Cheol Byun
Keyword(s):  
Low Dose ◽  
Binary Classification ◽  
Lung Nodule ◽  
Fine Grained ◽  
Internal Working ◽  
Nodule Detection ◽  
Classification Tasks ◽  
3D Cnn ◽  
Nodule Classification ◽  
Fold Cross Validation

The 3D convolutional neural network is able to make use of the full nonlinear 3D context information of lung nodule detection from the DICOM (Digital Imaging and Communications in Medicine) images, and the Gradient Class Activation has shown to be useful for tailoring classification tasks and localization interpretation for fine-grained features and visual explanation for the internal working. Gradient-weighted class activation plays a crucial role for clinicians and radiologists in terms of trusting and adopting the model. Practitioners not only rely on a model that can provide high precision but also really want to gain the respect of radiologists. So, in this paper, we explored the lung nodule classification using the improvised 3D AlexNet with lightweight architecture. Our network employed the full nature of the multiview network strategy. We have conducted the binary classification (benign and malignant) on computed tomography (CT) images from the LUNA 16 database conglomerate and database image resource initiative. The results obtained are through the 10-fold cross-validation. Experimental results have shown that the proposed lightweight architecture achieved a superior classification accuracy of 97.17% on LUNA 16 dataset when compared with existing classification algorithms and low-dose CT scan images as well.

Deep-learning reconstruction for ultra-low-dose lung CT: Volumetric measurement accuracy and reproducibility of artificial ground-glass nodules in a phantom study

2021 ◽  
Author(s):  
ryoji mikayama ◽  
Takashi Shirasaka ◽  
Tsukasa Kojima ◽  
Yuki Sakai ◽  
Hidetake Yabuuchi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Iterative Reconstruction ◽  
Measurement Accuracy ◽  
Low Dose ◽  
Lung Nodule ◽  
Ground Glass ◽  
Volumetric Measurement ◽  
Low Dose Ct ◽  
Nodule Volume ◽  
Ground Glass Nodules

Objectives The lung nodule volume determined by CT is used for nodule diagnoses and monitoring tumor responses to therapy. Increased image noise on low-dose CT degrades the measurement accuracy of the lung nodule volume. We compared the volumetric accuracy among deep-learning reconstruction (DLR), model-based iterative reconstruction (MBIR), and hybrid iterative reconstruction (HIR) at an ultra-low-dose setting. Methods Artificial ground-glass nodules (6 mm and 10 mm diameters, −660 HU) placed at the lung-apex and the middle-lung field in chest phantom were scanned by 320-row CT with the ultra-low-dose setting of 6.3 mAs. Each scan data set was reconstructed by DLR, MBIR, and HIR. The volumes of nodules were measured semi-automatically, and the absolute percent volumetric error (APEvol) was calculated. The APEvol provided by each reconstruction were compared by the Tukey-Kramer method. Inter- and intraobserver variabilities were evaluated by a Bland-Altman analysis with limits of agreements. Results DLR provided a lower APEvol compared to MBIR and HIR. The APEvol of DLR (1.36%) was significantly lower than those of the HIR (8.01%, p = 0.0022) and MBIR (7.30%, p = 0.0053) on a 10-mm-diameter middle-lung nodule. DLR showed narrower limits of agreement compared to MBIR and HIR in the inter- and intraobserver agreement of the volumetric measurement. Conclusions DLR showed higher accuracy compared to MBIR and HIR for the volumetric measurement of artificial ground-glass nodules by ultra-low-dose CT. Advances in knowledge DLR with ultra-low-dose setting allows a reduction of dose exposure, maintaining accuracy for the volumetry of lung nodule, especially in patients which deserve a long-term follow-up.

scholarly journals Automated Lung Nodule Detection at Low-Dose CT: Preliminary Experience

2003 ◽  
Vol 4 (4) ◽  
pp. 211 ◽  
Author(s):  
Jin Mo Goo ◽  
Jeong Won Lee ◽  
Hyun Ju Lee ◽  
Seunghwan Kim ◽  
Jong Hyo Kim ◽  
...  
Keyword(s):  
Low Dose ◽  
Lung Nodule ◽  
Low Dose Ct ◽  
Lung Nodule Detection ◽  
Nodule Detection

Application “Lung Nodule" Software With Lungrads On Early Detection And Follow Up The Pulmonary Nodules By Lung Low Dose CT Findings

2020 ◽  
Author(s):  
Ha Hoang Thi Ngoc
Keyword(s):  
Lung Cancer ◽  
Early Detection ◽  
Pulmonary Nodule ◽  
Low Dose ◽  
False Negative ◽  
Lung Nodule ◽  
Pulmonary Nodules ◽  
Lung Cancer Mortality ◽  
Smoking History ◽  
Low Dose Ct

Background: A pulmonary nodule is defined as a rounded or irregular opacity, well or poorly defined, measuring up to 3 cm in diameter. Early detection the malignancy of nodules has a significant role in decreasing the mortality, increasing the survival time and consider as early diagnosis lung cancer. Content: The main risk factors are those of current or former smokers, aged 55 to 74 years with a smoking history of at least 1 pack-day. Low dose CT: Screening individuals with high risk of lung cancer by low dose CT scans could reduce lung cancer mortality by 20 percent compared to chest X-ray. Radiation dose has to maximum reduced but respect the rule of ALARA (As Low as Resonably Archivable). ACR-LungRADS 2014: Classification of American College of Radiology, LungRADS, is a newly application but showed many advantages in comparison with others classification such as increasing positive predict value (PPV), no result of false negative and cost effectiveness. “Lung nodule” was applied as a smart phone application in order to have a quickly evaluation, especially the malignancy and management face on a pulmonary nodule.

scholarly journals Comparison of sensitivity of lung nodule detection between radiologists and technologists on low-dose CT lung cancer screening images

2012 ◽  
Vol 85 (1017) ◽  
pp. e603-e608 ◽  
Author(s):  
R Kakinuma ◽  
K Ashizawa ◽  
T Kobayashi ◽  
A Fukushima ◽  
H Hayashi ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
Low Dose ◽  
Lung Nodule ◽  
Lung Cancer Screening ◽  
Low Dose Ct ◽  
Lung Nodule Detection ◽  
Nodule Detection
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