Chapter title |
Unsupervised Discovery of Spatially-Informed Lung Texture Patterns for Pulmonary Emphysema: The MESA COPD Study
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Chapter number | 14 |
Book title |
Medical Image Computing and Computer Assisted Intervention − MICCAI 2017
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Published in |
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, September 2017
|
DOI | 10.1007/978-3-319-66182-7_14 |
Pubmed ID | |
Book ISBNs |
978-3-31-966181-0, 978-3-31-966182-7
|
Authors |
Jie Yang, Elsa D. Angelini, Pallavi P. Balte, Eric A. Hoffman, John H. M. Austin, Benjamin M. Smith, Jingkuan Song, R. Graham Barr, Andrew F. Laine, Yang, Jie, Angelini, Elsa D., Balte, Pallavi P., Hoffman, Eric A., Austin, John H. M., Smith, Benjamin M., Song, Jingkuan, Barr, R. Graham, Laine, Andrew F. |
Abstract |
Unsupervised discovery of pulmonary emphysema subtypes offers the potential for new definitions of emphysema on lung computed tomography (CT) that go beyond the standard subtypes identified on autopsy. Emphysema subtypes can be defined on CT as a variety of textures with certain spatial prevalence. However, most existing approaches for learning emphysema subtypes on CT are limited to texture features, which are sub-optimal due to the lack of spatial information. In this work, we exploit a standardized spatial mapping of the lung and propose a novel framework for combining spatial and texture information to discover spatially-informed lung texture patterns (sLTPs). Our spatial mapping is demonstrated to be a powerful tool to study emphysema spatial locations over different populations. The discovered sLTPs are shown to have high reproducibility, ability to encode standard emphysema subtypes, and significant associations with clinical characteristics. |
X Demographics
Geographical breakdown
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Unknown | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 16 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 4 | 25% |
Researcher | 3 | 19% |
Student > Postgraduate | 2 | 13% |
Lecturer | 1 | 6% |
Student > Master | 1 | 6% |
Other | 3 | 19% |
Unknown | 2 | 13% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 4 | 25% |
Engineering | 4 | 25% |
Medicine and Dentistry | 4 | 25% |
Neuroscience | 1 | 6% |
Unknown | 3 | 19% |