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



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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