# Volumetric landmark detection with a multi-scale shift equivariant neural network

arXiv: https://arxiv.org/abs/2003.01639 · HTML: https://mlsanity.com/paper/2003.01639/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2020.

## Built on by (most cited first, 9 of 9)

- [A Robust and Interpretable Deep Learning Framework for Multi-modal Registration via Keypoints](https://mlsanity.com/paper/2304.09941/) (2023)
- [Reliable uncertainty quantification for 2D/3D anatomical landmark localization using multi-output conformal prediction](https://mlsanity.com/paper/2503.14106/) (2025)
- [BigReg: An Efficient Registration Pipeline for High-Resolution X-Ray and Light-Sheet Fluorescence Microscopy](https://mlsanity.com/paper/2404.14807/) (2024)
- [CABLD: Contrast-Agnostic Brain Landmark Detection with Consistency-Based Regularization](https://mlsanity.com/paper/2411.17845/) (2024)
- [Self-supervised Landmark Learning with Deformation Reconstruction and Cross-subject Consistency Objectives](https://mlsanity.com/paper/2308.04987/) (2023)
- [Aortic root landmark localization with optimal transport loss for heatmap regression](https://mlsanity.com/paper/2407.04921/) (2024)
- [RealKeyMorph: Keypoints in Real-world Coordinates for Resolution-agnostic Image Registration](https://mlsanity.com/paper/2506.10344/) (2025)
- [DINOMotion: advanced robust tissue motion tracking with DINOv2 in 2D-Cine MRI-guided radiotherapy](https://mlsanity.com/paper/2508.10260/) (2025)
- [K-Stain: Keypoint-Driven Correspondence for H&E-to-IHC Virtual Staining](https://mlsanity.com/paper/2511.06709/) (2025)

## Abstract (arXiv)

Deep neural networks yield promising results in a wide range of computer vision applications, including landmark detection. A major challenge for accurate anatomical landmark detection in volumetric images such as clinical CT scans is that large-scale data often constrain the capacity of the employed neural network architecture due to GPU memory limitations, which in turn can limit the precision of the output. We propose a multi-scale, end-to-end deep learning method that achieves fast and memory-efficient landmark detection in 3D images. Our architecture consists of blocks of shift-equivariant networks, each of which performs landmark detection at a different spatial scale. These blocks are connected from coarse to fine-scale, with differentiable resampling layers, so that all levels can be trained together. We also present a noise injection strategy that increases the robustness of the model and allows us to quantify uncertainty at test time. We evaluate our method for carotid artery bifurcations detection on 263 CT volumes and achieve a better than state-of-the-art accuracy with mean Euclidean distance error of 2.81mm.