A shape-based approach to the segmentation of medical imagery using level sets

TLDR

The study proposes a shape‑based curve evolution method for segmenting medical images of known object types. The method models the segmentation curve parametrically using PCA on signed distance maps, then optimizes an objective function, and is applied to 2D cardiac MRI and 3D prostate MRI. The algorithm handles multidimensional data, manages topological changes, is robust to noise and initialization, computationally efficient, and eliminates the need for point correspondences during training.

Abstract

We propose a shape-based approach to curve evolution for the segmentation of medical images containing known object types. In particular, motivated by the work of Leventon, Grimson, and Faugeras (2000), we derive a parametric model for an implicit representation of the segmenting curve by applying principal component analysis to a collection of signed distance representations of the training data. The parameters of this representation are then manipulated to minimize an objective function for segmentation. The resulting algorithm is able to handle multidimensional data, can deal with topological changes of the curve, is robust to noise and initial contour placements, and is computationally efficient. At the same time, it avoids the need for point correspondences during the training phase of the algorithm. We demonstrate this technique by applying it to two medical applications; two-dimensional segmentation of cardiac magnetic resonance imaging (MRI) and three-dimensional segmentation of prostate MRI.