A survey on lung CT datasets and research trends

被引：0
作者：
Adiraju R.V. [1 ]
Elias S. [2 ]
机构：
[1] School of Electronics Engineering, Vellore Institute of Technology, Chennai
[2] Centre for Advanced Data Science, Vellore Institute of Technology, Chennai
来源：
Research on Biomedical Engineering | 2021年 / 37卷 / 2期
关键词：
Cancer imaging archive (TCIA); Classification of lung nodule; Lung CT dataset; Nodule detection; Non-small cell lung cancer (NSCLC)-Radiomics dataset; The lung image database consortium-image collection and image database resource initiative (LIDC-IDRI) dataset;
D O I：
10.1007/s42600-021-00138-3
中图分类号：
学科分类号：
摘要：
Purpose: Lung cancer is the most dangerous of all forms of cancer and it has the highest occurrence rate, world over. Early detection of lung cancer is a difficult task. Medical images generated by computer tomography (CT) are being used extensively for lung cancer analysis and research. However, it is essential to have a well-organized image database in order to design a reliable computer-aided diagnosis (CAD) tool. Identifying the most appropriate dataset for the research is another big challenge. Literarture review: The objective of this paper is to present a review of literature related to lung CT datasets. The Cancer Imaging Archive (TCIA) consortium collates different types of cancer datasets and permits public access through an integrated search engine. This survey summarizes the research work done using lung CT datasets maintained by TCIA. The motivation to present this survey was to help the research community in selecting the right lung dataset and to provide a comprehensive summary of the research developments in the field. © 2021, Sociedade Brasileira de Engenharia Biomedica.
引用
收藏
页码：403 / 418
页数：15
相关论文
共 99 条
[71]  
Shanthi T., Sabeenian R.S., Modified Alexnet architecture for classification of diabetic retinopathy images, Comput Electr. Eng, 76, (2019)
[72]  
Shaukat F., Raja G., Gooya A., Frangi A.F., Fully automatic detection of lung nodules in CT images using a hybrid feature set: med, Phys., 44, pp. 3615-3629, (2017)
[73]  
Shaukat F., Raja G., Ashraf R., Khalid S., Ahmad M., Ali A., Artificial neural network-based classification of lung nodules in CT images using intensity, shape and texture features, J Ambient Intell Human Comput, 10, pp. 4135-4149, (2019)
[74]  
Shayesteh S.P., Shiri I., Karami A.H., Hashemian R., Kooranifar S., Ghaznavi H., Et al., Predicting lung cancer patients’ survival time via logistic regression-based models in a quantitative radiomic framework, J. Biomed. Phys. Eng., (2019)
[75]  
Shen S., Bui A.A., Cong J., Hsu W., An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy, Computers in biology and medicine, 57, (2015)
[76]  
Shen W., Et al., Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification, Pattern Recognition, 61, (2017)
[77]  
Siegel R.L., Miller K.D., Jemal A., Cancer statistics, 2020, CA. Cancer J. Clin., 70, 1, (2020)
[78]  
Singadkar G., Mahajan A., Thakur M., Talbar S., Automatic lung segmentation for the inclusion of juxtapleural nodules and pulmonary vessels using curvature based border correction, J King Saud Univ - Comput Inf Sci, (2018)
[79]  
Sivakumar S., Chandrasekar C, (2013)
[80]  
Soh L.K., Tsatsoulis C., Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices, IEEE Transactions on Geoscience & Remote Sensing, 37, (1999)
12345678910