An atlas-navigated optimal medial axis and deformable model algorithm (NOMAD) for the segmentation of the optic nerves and chiasm in MR and CT images

In recent years, radiation therapy has become the preferred treatment for many types of head and neck tumors. To plan the procedure, vital structures, including the optic nerves and chiasm, must be identified using CT/MR imagery. In this work we present a novel method for automatically localizing the optic nerves and chiasm using a tubular structure localization algorithm in which a statistical model and image registration are used to incorporate a priori local intensity and shape information. The method results in mean Dice coefficients of 0.8 when compared to manual segmentations over ten test cases. This suggests that our method is more accurate than existing techniques developed for the segmentation of these structures.

To identify the optic pathways, we first identify the medial axes using a novel optimal path-based approach. The level set expansions of these axes can be combined to produce a final segmentation..Figure optionsDownload high-quality image (119 K)Download as PowerPoint slideHighlights
► We test a tubular structure localization algorithm.
► A statistical model incorporates a priori local intensity and shape information.
► Segmentation of the optic nerves and chiasm results in dice coefficients of 0.8.
► Results suggest that our approach is more accurate than existing techniques.