ning xiao

Ph.D

Taiyuan University of Technology

Researcher

NLST (Learn more about this study)

NLST-509

May 21, 2019

Study on the Growth and Evolution of Pulmonary Focus on Long-term CT Imaging

The study on long-term lung cancer image data (complete progression of development of disease over time) is the key to early diagnosis of lung cancer. At present,the reveal of characteristics and laws of lung Cancer evolution over time is not comprehensive and the related course of disease of image data and evolution mechanism over time is not ambiguous, which cannot play a guiding role on early diagnosis of lung cancer. Aiming at this problem, the project intends to implement the following researches: the study on tracking and extraction of cross-period sequences of pulmonary lesions in long-term CT images, and to explore a deep learning method for lung cancer staging in which spatial multivariate features are combined under the key attributes of medical records; the study on the dependent relationship of the 3D feature of lesions on the time dimension, and to explore the method of accurately tracing early sequence images of lung cancer in middle and late stage sequence images and the modeling mechanism of early lesion characteristics over time; the study on the relationship between long-term CT sequence image data and diagnostic information features including time series and disease stages, and to explore the effectiveness of LSTM depth framework under time-incomplete data.