Automatically generated, objective, and reproducible CT imaging-based biomarkers are critical for the differentiation of sub-classes of pulmonary disease to better link phenotypes to genotypes, enabling the development of new treatments for lung diseases like chronic obstructive pulmonary disease (COPD), interstitial pulmonary fibrosis, sarcoidosis, or asthma. While previous research on developing image-based biomarkers for COPD and asthma has shown promising results, the translation of already developed CT biomarkers for disease entities with high radio density pathology caused by significant inflammation, consolidation, alveolar flooding or fibro- sis was not successful up to now due to the lack of automated robust lung image analysis techniques. This shortcoming not only hinders the utilization of existing CT biomarkers, it is also problematic for developing new image-based biomarkers for these lung diseases. The objective of this proposal is to address this bottleneck in quantitative lung image analysis by developing robust and fast image analysis techniques for single CT scan and high/low volume CT scan pairs. Specifically, novel methods for lung, lung lobe, and airway segmentation will be developed and validated, which can tolerate high density lung pathology and are a key component required for calculating CT biomarkers for lung path anatomy. Robustness of lung and lobe segmentation methods will be achieved by utilizing model-based image analysis methods. Up to now, such approaches were considered as too computationally demanding for lung image analysis because of the large organ size. We will address this issue by utilizing general-purpose computation on graphics processing hardware techniques, allowing us to reduce computation times significantly, as demonstrated by preliminary results. By providing an efficient means of objectively identifying lung structures, these structures can be quantified and utilized for sub-phenotyping patient populations, as required to facilitate large multi-center studies with 20,000 or more subjects.
Automated lung CT image analysis technology developed within this project will enable utilizing objective image-based biomarkers for lung diseases like interstitial pulmonary fibrosis, sarcoidosis, severe asthma, or COPD in large multi-center studies.
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|Gill, Gurman; Beichel, Reinhard R (2016) An approach for reducing the error rate in automated lung segmentation. Comput Biol Med 76:143-53|
|Gill, Gurman; Beichel, Reinhard R (2015) Lung Segmentation in 4D CT Volumes Based on Robust Active Shape Model Matching. Int J Biomed Imaging 2015:125648|
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|Gill, Gurman; Toews, Matthew; Beichel, Reinhard R (2014) Robust Initialization of Active Shape Models for Lung Segmentation in CT Scans: A Feature-Based Atlas Approach. Int J Biomed Imaging 2014:479154|
|Sun, Shanhui; Sonka, Milan; Beichel, Reinhard R (2013) Graph-based IVUS segmentation with efficient computer-aided refinement. IEEE Trans Med Imaging 32:1536-49|
|Sun, Shanhui; Sonka, Milan; Beichel, Reinhard R (2013) Lung segmentation refinement based on optimal surface finding utilizing a hybrid desktop/virtual reality user interface. Comput Med Imaging Graph 37:15-27|
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