Automatic annotation of radiological observations in liver CT images

AMIA Annu Symp Proc. 2012;2012:257-63. Epub 2012 Nov 3.


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 order 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.

Publication types

  • Comparative Study

MeSH terms

  • Area Under Curve
  • Artificial Intelligence*
  • Computational Biology*
  • Humans
  • Liver / diagnostic imaging*
  • Liver Diseases / diagnostic imaging*
  • Logistic Models*
  • Semantics
  • Tomography, X-Ray Computed*
  • Vocabulary, Controlled