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Supervised Probabilistic Segmentation of Pulmonary Nodules in CT Scans

  • Bram van Ginneken
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)

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.

Keywords

Pulmonary Nodule Seed Point Multislice Compute Tomography Solid Nodule Small Pulmonary Nodule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Bram van Ginneken
    • 1
  1. 1.Image Sciences InstituteUniversity Medical Center UtrechtThe Netherlands

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