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

arXiv: https://arxiv.org/abs/2308.04987 · HTML: https://mlsanity.com/paper/2308.04987/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 9 Aug 2023.

## Builds on (most cited first, 10 of 10)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Kernel Operations on the GPU, with Autodiff, without Memory Overflows](https://mlsanity.com/paper/2004.11127/) (2020)
- [Numerical Coordinate Regression with Convolutional Neural Networks](https://mlsanity.com/paper/1801.07372/) (2018)
- [Networks for Joint Affine and Non-parametric Image Registration](https://mlsanity.com/paper/1903.08811/) (2019)
- [textttGradICON: Approximate Diffeomorphisms via Gradient Inverse Consistency](https://mlsanity.com/paper/2206.05897/) (2022)
- [DeepSSM: A Deep Learning Framework for Statistical Shape Modeling from Raw Images](https://mlsanity.com/paper/1810.00111/) (2018)
- [Volumetric landmark detection with a multi-scale shift equivariant neural network](https://mlsanity.com/paper/2003.01639/) (2020)
- [Self-Supervised Discovery of Anatomical Shape Landmarks](https://mlsanity.com/paper/2006.07525/) (2020)
- [Learning Deep Features for Shape Correspondence with Domain Invariance](https://mlsanity.com/paper/2102.10493/) (2021)

## Built on by (most cited first, 1 of 1)

- [CABLD: Contrast-Agnostic Brain Landmark Detection with Consistency-Based Regularization](https://mlsanity.com/paper/2411.17845/) (2024)

## Abstract (arXiv)

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.