Share Email Print

Proceedings Paper

Identification of early-stage usual interstitial pneumonia from low-dose chest CT scans using fractional high-density lung distribution
Author(s): Yiting Xie; Mary Salvatore; Shuang Liu; Artit Jirapatnakul; David F. Yankelevitz; Claudia I. Henschke; Anthony P. Reeves
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

A fully-automated computer algorithm has been developed to identify early-stage Usual Interstitial Pneumonia (UIP) using features computed from low-dose CT scans. In each scan, the pre-segmented lung region is divided into N subsections (N = 1, 8, 27, 64) by separating the lung from anterior/posterior, left/right and superior/inferior in 3D space. Each subsection has approximately the same volume. In each subsection, a classic density measurement (fractional high-density volume h) is evaluated to characterize the disease severity in that subsection, resulting in a feature vector of length N for each lung. Features are then combined in two different ways: concatenation (2*N features) and taking the maximum in each of the two corresponding subsections in the two lungs (N features).

The algorithm was evaluated on a dataset consisting of 51 UIP and 56 normal cases, a combined feature vector was computed for each case and an SVM classifier (RBF kernel) was used to classify them into UIP or normal using ten-fold cross validation. A receiver operating characteristic (ROC) area under the curve (AUC) was used for evaluation. The highest AUC of 0.95 was achieved by using concatenated features and an N of 27. Using lung partition (N = 27, 64) with concatenated features had significantly better result over not using partitions (N = 1) (p-value < 0.05). Therefore this equal-volume partition fractional high-density volume method is useful in distinguishing early-stage UIP from normal cases.

Paper Details

Date Published: 3 March 2017
PDF: 8 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013408 (3 March 2017); doi: 10.1117/12.2254126
Show Author Affiliations
Yiting Xie, Cornell Univ. (United States)
Mary Salvatore, Icahn School of Medicine at Mount Sinai (United States)
Shuang Liu, Cornell Univ. (United States)
Artit Jirapatnakul, Icahn School of Medicine at Mount Sinai (United States)
David F. Yankelevitz, Icahn School of Medicine at Mount Sinai (United States)
Claudia I. Henschke, Icahn School of Medicine at Mount Sinai (United States)
Anthony P. Reeves, Cornell Univ. (United States)

Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato III; Nicholas A. Petrick, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?