Geodesic Shape-Based Averaging

Abstract

Abstract.A new method for the geometrical averaging of labels or landmarks is presented. This method expands the shape-based averaging [1] framework from an Euclidean to a geodesic based distance, incorporating a spatially varying similarity term as time cost. This framework has unique geometrical properties, making it ideal for propagating very small structures following rigorous labelling protocols. The method is used to automate the seeding and way-pointing of optic radiation tractography in DTI imaging. The propagated seeds and waypoints follow a strict clinical protocol by being geometrically constrained to one single slice and by guaranteeing spatial contiguity. The proposed method not only reduces the fragmentation of the propagated areas but also significantly increases the seed positioning accuracy and subsequent tractography results when compared to state-of-the-art label fusion techniques.

Weighted Shape-Based Averaging With Neighborhood Prior Model for Multiple Atlas Fusion-Based Medical Image Segmentation

“…We have noticed that in a very recent work [10], a geodesic extension of SBA is proposed in the context of DTI tractography that includes a similarity measure into SBA. However, the goals and formulations of their work are different from our current work.…”

“…However, the goals and formulations of their work are different from our current work. In addition, [10] does not incorporate any neighborhood prior model that we are now going to present.…”