Supervised Probabilistic Segmentation of Pulmonary Nodules in CT Scans
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.
KeywordsPulmonary Nodule Seed Point Multislice Compute Tomography Solid Nodule Small Pulmonary Nodule
- 4.Kuhnigk, J.M., Dicken, V., Bornemann, L., Wormanns, D., Krass, S., Peitgen, H.O.: Fast automated segmentation and reproducible volumetry of pulmonary metastases in CT-scans for therapy monitoring. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3217, pp. 933–941. Springer, Heidelberg (2004)CrossRefGoogle Scholar
- 9.Wormanns, D., Diederich, S.: Characterization of small pulmonary nodules by CT. Eur. Radiol. 14, 1380–1391 (2004)Google Scholar
- 10.Armato, S.G., McLennan, G., McNitt-Gray, M.F., Meyer, C.R., Yankelevitz, D., Aberle, D.R., Henschke, C.I., Hoffman, E.A., Kazerooni, E.A., MacMahon, H., Reeves, A.P., Croft, B.Y., Clarke, L.P.: Lung Image Database Consortium: Developing a resource for the medical imaging research community. Radiology 232(3), 739–748 (2004)CrossRefGoogle Scholar