S3M: scalable statistical shape modeling through unsupervised correspondences
Statistical shape models (SSMs) are an established way to represent the anatomy of a
population with various clinically relevant applications. However, they typically require …
population with various clinically relevant applications. However, they typically require …
Advancing Perception in Artificial Intelligence through Principles of Cognitive Science
Although artificial intelligence (AI) has achieved many feats at a rapid pace, there still exist
open problems and fundamental shortcomings related to performance and resource …
open problems and fundamental shortcomings related to performance and resource …
Self-supervised Landmark Learning with Deformation Reconstruction and Cross-Subject Consistency Objectives
CH Chao, M Niethammer - International Workshop on PRedictive …, 2023 - Springer
Abstract A Point Distribution Model (PDM) is the basis of a Statistical Shape Model (SSM)
that relies on a set of landmark points to represent a shape and characterize the shape …
that relies on a set of landmark points to represent a shape and characterize the shape …
3D Shape Correspondence for Medical Applications Using Neural Descriptor Fields
The problem this paper is concerned with is that of unsupervised learning for point cloud
representation which can be used to build anatomy correspondence without need for …
representation which can be used to build anatomy correspondence without need for …
Check for updates Self-supervised Landmark Learning with Deformation Reconstruction and Cross-Subject Consistency Objectives
CH Chao, M Niethammer - Predictive Intelligence in Medicine …, 2023 - books.google.com
A Point Distribution Model (PDM) is the basis of a Statistical Shape Model (SSM) that relies
on a set of landmark points to represent a shape and characterize the shape variation. In this …
on a set of landmark points to represent a shape and characterize the shape variation. In this …