Statistical shape models (SSMs) have by now been firmly established as a robust tool for
segmentation of medical images. While 2D models have been in use since the early 1990s …

Whole heart segmentation from magnetic resonance imaging or computed tomography is a
prerequisite for many clinical applications. Since manual delineation can be tedious and …

We introduce a method for training neural networks to perform image or volume
segmentation in which prior knowledge about the topology of the segmented object can be …

Abstract Convolutional Neural Networks (CNNs) work very well for supervised learning
problems when the training dataset is representative of the variations expected to be …

A thoroughly revised and updated edition of this introduction to modern statistical methods
for shape analysis Shape analysis is an important tool in the many disciplines where objects …

We review methods designed to compute correspondences between geometric shapes
represented by triangle meshes, contours or point sets. This survey is motivated in part by …

A primary goal of statistical shape analysis is to describe the variability of a population of
geometric objects. A standard technique for computing such descriptions is principal …

The notion of using context information for solving high-level vision and medical image
segmentation problems has been increasingly realized in the field. However, how to learn …

We propose a nonparametric, probabilistic model for the automatic segmentation of medical
images, given a training set of images and corresponding label maps. The resulting …

Given a shape, a skeleton is a thin centered structure which jointly describes the topology
and the geometry of the shape. Skeletons provide an alternative to classical boundary or …