A Software for the Lung Image Database Consortium and Image Database Resource Initiative

With the development of big data to medical area, more and more researchers use authoritative public datasets for research. In the field of lung cancer research, Lung Image Database Consortium and Image Database Resource Initiative is the largest open lung image database in the world, which contains CT images stored in DICOM format and expert diagnostic information stored in XML format. However, data cannot be used directly and needs to be further processed. To solve this problem, a preprocessing software based on lung CT image data is designed. The software can realize the preprocessing of lung CT image, interprets the expert diagnosis information completely, and visualizes the expert annotation results. The lung CT image data preprocessing software has cross-platform portability, openness and sharing.

[1]  E. V. van Beek,et al.  The Lung Image Database Consortium (LIDC): a comparison of different size metrics for pulmonary nodule measurements. , 2007, Academic radiology.

[2]  Shinichi Tamura,et al.  Automated lung segmentation and smoothing techniques for inclusion of juxtapleural nodules and pulmonary vessels on chest CT images , 2014, Biomed. Signal Process. Control..

[3]  Shengwen Guo,et al.  Automatic Segmentation and Quantitative Diagnosis of Pulmonary Parenchyma in Thoracic CT , 2007, 2007 1st International Conference on Bioinformatics and Biomedical Engineering.

[4]  Lan Lin,et al.  [Research progress on computed tomography image detection and classification of pulmonary nodule based on deep learning]. , 2019, Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi.

[5]  Hongli Lin,et al.  Data analysis of the Lung Imaging Database Consortium and Image Database Resource Initiative. , 2015, Academic radiology.

[6]  A. Jemal,et al.  Cancer statistics, 2019 , 2019, CA: a cancer journal for clinicians.

[7]  Jiemin Liu,et al.  Transformation of fuzzy spatiotemporal data from XML to object-oriented database , 2018, Earth Science Informatics.

[8]  K. Doi,et al.  Computer-aided diagnosis of pulmonary nodules: results of a large-scale observer test. , 1999, Radiology.

[9]  Thomas Schwentick,et al.  Reasoning About XML Constraints Based on XML-to-Relational Mappings , 2018, Theory of Computing Systems.

[10]  K. Doi,et al.  Improved detection of lung nodules on chest radiographs using a commercial computer-aided diagnosis system. , 2004, AJR. American journal of roentgenology.

[11]  Ching-Wei Wang,et al.  Automated morphological classification of lung cancer subtypes using H&E tissue images , 2012, Machine Vision and Applications.

[12]  W. Heindel,et al.  Detection of pulmonary nodules at spiral CT: comparison of maximum intensity projection sliding slabs and single-image reporting , 2001, European Radiology.

[13]  C. Hsia,et al.  Regional lung growth following pneumonectomy assessed by computed tomography. , 2004, Journal of applied physiology.

[14]  Heber MacMahon,et al.  The Lung Image Database Consortium (LIDC): ensuring the integrity of expert-defined "truth". , 2007, Academic radiology.

[15]  Richard C. Pais,et al.  The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. , 2011, Medical physics.

[16]  N. Dubrawsky Cancer statistics , 1989, CA: a cancer journal for clinicians.

[17]  Hongli Lin,et al.  XML Schemas Representation of DICOM Data Model , 2010, 2010 4th International Conference on Bioinformatics and Biomedical Engineering.