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Computational and Mathematical Methods in Medicine
Volume 2013 (2013), Article ID 683216, 9 pages
http://dx.doi.org/10.1155/2013/683216
Research Article

Discriminative Random Field Segmentation of Lung Nodules in CT Studies

1Cornell University, Ithaca, NY 14853, USA
2Weill Cornell Medical College, New York, NY 10065, USA

Received 20 March 2013; Revised 2 June 2013; Accepted 15 June 2013

Academic Editor: Tianye Niu

Copyright © 2013 Brian Liu and Ashish Raj. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The ability to conduct high-quality semiautomatic 3D segmentation of lung nodules in CT scans is of high value to busy radiologists. Discriminative random fields (DRFs) were used to segment 3D volumes of lung nodules in CT scan data using only one seed point per nodule. Optimal parameters for the DRF inference were first found using simulated annealing. These parameters were then used to solve the inference problem using the graph cuts algorithm. Results of the segmentation exhibited high precision and recall. The system can be adapted to facilitate the process of longitudinal studies but will still require human checking for failed cases.