3D level set esophagus segmentation in thoracic CT images using spatial, appearance and shape models

Authors:
Sila Kurugol;Jennifer G. Dy;Gregory C. Sharp;Dana H. Brooks
Affiliations:
ECE Dept., Northeastern University, Boston, MA;ECE Dept., Northeastern University, Boston, MA;Dept. of Radiation Oncology, Mass. General Hospital and Harvard Medical School, Boston, MA;ECE Dept., Northeastern University, Boston, MA
Venue:
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Year:
2010

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CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01

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Abstract

We propose a 3D segmentation algorithm to locate the esophagus in thoracic CT scans using a learning based approach. To ease the training data requirement and allow maximum inter-subject flexibility, we built a simple algorithm based on normalization to anatomical reference points to match a training set of thoracic CTs instead of a full statistical registration based on neighboring structures. We use spatial and appearance models to locate the centerline. We build a shape model by subtracting the centerline and applying PCA to the training data sets. The shape model includes a mean shape plus the weighted combination of modes. To locate the esophageal wall, we optimize a cost function including terms for appearance, shape model, smoothness constraints and air/contrast model using a 3D level set framework.