Automatic annotation of radiological observations in liver CT images

Francisco Gimenez, Jiajing Xu, Yi Liu, Tiffany Liu, Christopher Beaulieu, Daniel Rubin, Sandy Napel
2012 AMIA Annual Symposium Proceedings  
We aim to predict radiological observations using computationally-derived imaging features extracted from computed tomography (CT) images. We created a dataset of 79 CT images containing liver lesions identified and annotated by a radiologist using a controlled vocabulary of 76 semantic terms. Computationally-derived features were extracted describing intensity, texture, shape, and edge sharpness. Traditional logistic regression was compared to L(1)-regularized logistic regression (LASSO) in
more » ... er to predict the radiological observations using computational features. The approach was evaluated by leave one out cross-validation. Informative radiological observations such as lesion enhancement, hypervascular attenuation, and homogeneous retention were predicted well by computational features. By exploiting relationships between computational and semantic features, this approach could lead to more accurate and efficient radiology reporting.
pmid:23304295 pmcid:PMC3540508 fatcat:d47tnclsgjdxpbzogqfltse6ie