Supervised probabilistic segmentation of pulmonary nodules in CT scans

B. van Ginneken

in: Medical Image Computing and Computer-Assisted Intervention, volume 4191 of Lecture Notes in Computer Science, 2006, pages 912-919

DOI PMID

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.

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