Supervised probabilistic segmentation of pulmonary nodules in CT scans

Med Image Comput Comput Assist Interv. 2006;9(Pt 2):912-9. doi: 10.1007/11866763_112.

Abstract

An automatic method for lung nodule segmentation from computed tomography (CT) data is presented that is different from previous work in several respects. Firstly, it is supervised; it learns how to obtain a reliable segmentation from examples in a training phase. Secondly, the method provides a soft, or probabilistic segmentation, thus taking into account the uncertainty inherent in this segmentation task. The method is trained and tested on a public data set of 23 nodules for which soft labelings are available. The new method is shown to outperform a previously published conventional method. By merely changing the training data, non-solid nodules can also be segmented.

Publication types

  • Evaluation Study

MeSH terms

  • Algorithms*
  • Artificial Intelligence*
  • Humans
  • Lung Neoplasms / diagnostic imaging
  • Pattern Recognition, Automated / methods*
  • Radiographic Image Enhancement / methods*
  • Radiographic Image Interpretation, Computer-Assisted / methods*
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Solitary Pulmonary Nodule / diagnostic imaging*
  • Tomography, X-Ray Computed / methods*