Paper Info
Title
Predicting LIDC diagnostic characteristics by combining spatial and diagnostic opinions
Abstract
Computer-aided diagnostic characterization (CADc) aims to support medical imaging decision making by objectively rating the radiologists' subjective, perceptual opinions of visual diagnostic characteristics of suspicious lesions. This research uses the publicly available Lung Image Database Consortium (LIDC) collection of radiologists' outlines of nodules and ratings of boundary and shape characteristics: spiculation, margin, lobulation, and sphericity. The approach attempts to reduce the observed disagreement between radiologists on the extent of nodules by combining their spatial opinion using probability maps to create regions of interest (ROIs). From these ROIs, images features are extracted and combined using machine learning models to predict a combined opinion, the median rating and a thresholded, binary version of their diagnostic characteristics. The results show slight to fair agreement-linear-weighted Kappa-between the CADc models and median radiologist opinion for the full scale five-level rating and fair to moderate agreement using a binary version of the median radiologist opinion.
Year
DOI
Venue
2010
10.1117/12.844009
Proceedings of SPIE
Keywords
Field
DocType
Characterization,Image Analysis,Feature Extraction,Region of Interest,Computer aided diagnosis
Computer vision,Medical imaging,Computer-aided diagnosis,Feature extraction,Artificial intelligence,Image database,Region of interest,Computing systems,Physics
Conference
Volume
ISSN
Citations 
7624
0277-786X
4
PageRank 
References 
Authors
0.42
0
3
Name
Order
Citations
PageRank
William H. Horsthemke1232.75
Daniela Stan Raicu246946.22
Jacob D. Furst354556.63