We present a method for the automated segmentation of knee bones and cartilage from
magnetic resonance imaging (MRI) that combines a priori knowledge of anatomical shape with …

Vertebral labelling and segmentation are two fundamental tasks in an automated spine
processing pipeline. Reliable and accurate processing of spine images is expected to benefit …

In our chapter we are describing how to reconstruct three-dimensional anatomy from medical
image data and how to build Statistical 3D Shape Models out of many such reconstructions …

We propose a novel Riemannian framework for statistical analysis of shapes that is able to
account for the nonlinearity in shape variation. By adopting a physical perspective, we …

Statistical shape modeling aims at capturing shape variations of an anatomical structure that
occur within a given population. Shape models are employed in many tasks, such as shape …

In this paper we report the challenge set-up and results of the Large Scale Vertebrae
Segmentation Challenge (VerSe) organized in conjunction with the MICCAI 2019. The challenge …

We present a novel approach for nonlinear statistical shape modeling that is invariant under
Euclidean motion and thus alignment-free. By analyzing metric distortion and curvature of …

This paper presents the outcomes of a contest organized to evaluate methods for the online
recognition of heterogeneous gestures from sequences of 3D hand poses. The task is the …

Convolutional neural networks (CNNs) are the state-of-the-art for automated assessment of
knee osteoarthritis (KOA) from medical image data. However, these methods lack …

We propose a generic spatiotemporal framework to analyze manifold-valued measurements,
which allows for employing an intrinsic and computationally efficient Riemannian …