Semantics and image content integration for pulmonary nodule interpretation in thoracic computed tomography

Daniela S. Raicu; Ekarin Varutbangkul; Janie G. Cisneros; Jacob D. Furst; David S. Channin; Samuel G. Armato III

doi:10.1117/12.710290

3 March 2007 Semantics and image content integration for pulmonary nodule interpretation in thoracic computed tomography

Daniela S. Raicu, Ekarin Varutbangkul, Janie G. Cisneros, Jacob D. Furst, David S. Channin, Samuel G. Armato III

Author Affiliations +

Proceedings Volume 6512, Medical Imaging 2007: Image Processing; 65120S (2007) https://doi.org/10.1117/12.710290
Event: Medical Imaging, 2007, San Diego, CA, United States

Abstract

Useful diagnosis of lung lesions in computed tomography (CT) depends on many factors including the ability of radiologists to detect and correctly interpret the lesions. Computer-aided Diagnosis (CAD) systems can be used to increase the accuracy of radiologists in this task. CAD systems are, however, trained against ground truth and the mechanisms employed by the CAD algorithms may be distinctly different from the visual perception and analysis tasks of the radiologist. In this paper, we present a framework for finding the mappings between human descriptions and characteristics and computed image features. The data in our study were generated from 29 thoracic CT scans collected by the Lung Image Database Consortium (LIDC). Every case was annotated by up to 4 radiologists by marking the contour of nodules and assigning nine semantic terms to each identified nodule; fifty-nine image features were extracted from each segmented nodule. Correlation analysis and stepwise multiple regression were applied to find correlations among semantic characteristics and image features and to generate prediction models for each characteristic based on image features. From our preliminary experimental results, we found high correlations between different semantic terms (margin, texture), and promising mappings from image features to certain semantic terms (texture, lobulation, spiculation, malignancy). While the framework is presented with respect to the interpretation of pulmonary nodules in CT images, it can be easily extended to find mappings for other modalities in other anatomical structures and for other image features.

Citation Download Citation

Daniela S. Raicu, Ekarin Varutbangkul, Janie G. Cisneros, Jacob D. Furst, David S. Channin, and Samuel G. Armato III "Semantics and image content integration for pulmonary nodule interpretation in thoracic computed tomography", Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 65120S (3 March 2007); https://doi.org/10.1117/12.710290

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE