Self-supervised Landmark Learning with Deformation Reconstruction and Cross-subject Consistency Objectives

TL;DR

This paper introduces a self-supervised method for extracting anatomically consistent landmarks from registration models, improving shape modeling and deformation analysis in complex biological data.

Contribution

It proposes a novel landmark discovery loss and a registration-based approach that enhances landmark accuracy and consistency across subjects.

Findings

Outperforms existing image-based and point-based methods in osteoarthritis progression prediction

Achieves more accurate and consistent landmark detection across subjects

Demonstrates improved shape and deformation modeling in complex biological data

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 variation. In this work, we present a self-supervised approach to extract landmark points from a given registration model for the PDMs. Based on the assumption that the landmarks are the points that have the most influence on registration, existing works learn a point-based registration model with a small number of points to estimate the landmark points that influence the deformation the most. However, such approaches assume that the deformation can be captured by point-based registration and quality landmarks can be learned solely with the deformation capturing objective. We argue that data with complicated deformations can not easily be modeled with point-based registration when only a limited number of points is used to extract influential landmark points. Further, landmark consistency is not assured in existing approaches In contrast, we propose to extract landmarks based on a given registration model, which is tailored for the target data, so we can obtain more accurate correspondences. Secondly, to establish the anatomical consistency of the predicted landmarks, we introduce a landmark discovery loss to explicitly encourage the model to predict the landmarks that are anatomically consistent across subjects. We conduct experiments on an osteoarthritis progression prediction task and show our method outperforms existing image-based and point-based approaches.