# Siamese Networks with Location Prior for Landmark Tracking in Liver Ultrasound Sequences

arXiv: https://arxiv.org/abs/1901.08109 · HTML: https://mlsanity.com/paper/1901.08109/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2019.

## Built on by (most cited first, 6 of 6)

- [Modality specific U-Net variants for biomedical image segmentation: A survey](https://mlsanity.com/paper/2107.04537/) (2021)
- [Transformer Lesion Tracker](https://mlsanity.com/paper/2206.06252/) (2022)
- [Probabilistic Spatial Analysis in Quantitative Microscopy with Uncertainty-Aware Cell Detection using Deep Bayesian Regression of Density Maps](https://mlsanity.com/paper/2102.11865/) (2021)
- [Landmark Tracking in Liver US images Using Cascade Convolutional Neural Networks with Long Short-Term Memory](https://mlsanity.com/paper/2209.06952/) (2022)
- [LSDM: Long-Short Diffeomorphic Motion for Weakly-Supervised Ultrasound Landmark Tracking](https://mlsanity.com/paper/2301.04748/) (2023)
- [Towards AI Lesion Tracking in PET/CT Imaging: A Siamese-based CNN Pipeline applied on PSMA PET/CT Scans](https://mlsanity.com/paper/2406.09327/) (2024)

## Abstract (arXiv)

Image-guided radiation therapy can benefit from accurate motion tracking by ultrasound imaging, in order to minimize treatment margins and radiate moving anatomical targets, e.g., due to breathing. One way to formulate this tracking problem is the automatic localization of given tracked anatomical landmarks throughout a temporal ultrasound sequence. For this, we herein propose a fully-convolutional Siamese network that learns the similarity between pairs of image regions containing the same landmark. Accordingly, it learns to localize and thus track arbitrary image features, not only predefined anatomical structures. We employ a temporal consistency model as a location prior, which we combine with the network-predicted location probability map to track a target iteratively in ultrasound sequences. We applied this method on the dataset of the Challenge on Liver Ultrasound Tracking (CLUST) with competitive results, where our work is the first to effectively apply CNNs on this tracking problem, thanks to our temporal regularization.