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

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4 Author(s)
Kurugol, S. ; ECE Dept., Northeastern Univ., Boston, MA, USA ; Dy, J.G. ; Sharp, G.C. ; Brooks, D.H.

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.

Published in:

Biomedical Imaging: From Nano to Macro, 2010 IEEE International Symposium on

Date of Conference:

14-17 April 2010

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