# KNEEL: Knee Anatomical Landmark Localization Using Hourglass Networks

arXiv: https://arxiv.org/abs/1907.12237 · HTML: https://mlsanity.com/paper/1907.12237/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2019.

## Built on by (most cited first, 14 of 14)

- [Unsupervised Multi-object Segmentation Using Attention and Soft-argmax](https://mlsanity.com/paper/2205.13271/) (2022)
- [A Unified Model for Longitudinal Multi-Modal Multi-View Prediction with Missingness](https://mlsanity.com/paper/2403.12211/) (2024)
- [Is in-domain data beneficial in transfer learning for landmarks detection in x-ray images?](https://mlsanity.com/paper/2403.01470/) (2024)
- [AdaTriplet: Adaptive Gradient Triplet Loss with Automatic Margin Learning for Forensic Medical Image Matching](https://mlsanity.com/paper/2205.02849/) (2022)
- [Self-supervised pre-training with diffusion model for few-shot landmark detection in x-ray images](https://mlsanity.com/paper/2407.18125/) (2024)
- [Deep Learning-based Alignment Measurement in Knee Radiographs](https://mlsanity.com/paper/2506.18209/) (2025)
- [Image prediction of disease progression by style-based manifold extrapolation](https://mlsanity.com/paper/2111.11439/) (2021)
- [MRIS: A Multi-modal Retrieval Approach for Image Synthesis on Diverse Modalities](https://mlsanity.com/paper/2303.10249/) (2023)
- [End-To-End Prediction of Knee Osteoarthritis Progression With Multi-Modal Transformers](https://mlsanity.com/paper/2307.00873/) (2023)
- [Risk Estimation of Knee Osteoarthritis Progression via Predictive Multi-task Modelling from Efficient Diffusion Model using X-ray Images](https://mlsanity.com/paper/2506.14560/) (2025)
- [Aortic root landmark localization with optimal transport loss for heatmap regression](https://mlsanity.com/paper/2407.04921/) (2024)
- [Toward Cost-efficient Adaptive Clinical Trials in Knee Osteoarthritis with Reinforcement Learning](https://mlsanity.com/paper/2408.02349/) (2024)
- [The Role of Radiographic Knee Alignment in Total Knee Replacement Outcomes and Opportunities for Artificial Intelligence-Driven Assessment](https://mlsanity.com/paper/2508.10941/) (2025)
- [Landmark-free Assessment of Lower-limb Alignment with Implicit Neural Shape Functions from Knee Radiographs](https://mlsanity.com/paper/2606.15250/) (2026)

## Abstract (arXiv)

This paper addresses the challenge of localization of anatomical landmarks in knee X-ray images at different stages of osteoarthritis (OA). Landmark localization can be viewed as regression problem, where the landmark position is directly predicted by using the region of interest or even full-size images leading to large memory footprint, especially in case of high resolution medical images. In this work, we propose an efficient deep neural networks framework with an hourglass architecture utilizing a soft-argmax layer to directly predict normalized coordinates of the landmark points. We provide an extensive evaluation of different regularization techniques and various loss functions to understand their influence on the localization performance. Furthermore, we introduce the concept of transfer learning from low-budget annotations, and experimentally demonstrate that such approach is improving the accuracy of landmark localization. Compared to the prior methods, we validate our model on two datasets that are independent from the train data and assess the performance of the method for different stages of OA severity. The proposed approach demonstrates better generalization performance compared to the current state-of-the-art.