# Comparison of Evaluation Metrics for Landmark Detection in CMR Images

arXiv: https://arxiv.org/abs/2201.10410 · HTML: https://mlsanity.com/paper/2201.10410/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 12 references that are in the map.
- First posted on arXiv 25 Jan 2022.

## Builds on (most cited first, 2 of 2)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Common Limitations of Image Processing Metrics: A Picture Story](https://mlsanity.com/paper/2104.05642/) (2021)

## Built on by (most cited first, 3 of 3)

- [Self-supervised motion descriptor for cardiac phase detection in 4D CMR based on discrete vector field estimations](https://mlsanity.com/paper/2209.05778/) (2022)
- [mvHOTA: A multi-view higher order tracking accuracy metric to measure spatial and temporal associations in multi-point detection](https://mlsanity.com/paper/2206.09372/) (2022)
- [Deformable Image Registration for Self-supervised Cardiac Phase Detection in Multi-View Multi-Disease Cardiac Magnetic Resonance Images](https://mlsanity.com/paper/2510.05819/) (2025)

## Abstract (arXiv)

Cardiac Magnetic Resonance (CMR) images are widely used for cardiac diagnosis and ventricular assessment. Extracting specific landmarks like the right ventricular insertion points is of importance for spatial alignment and 3D modeling. The automatic detection of such landmarks has been tackled by multiple groups using Deep Learning, but relatively little attention has been paid to the failure cases of evaluation metrics in this field. In this work, we extended the public ACDC dataset with additional labels of the right ventricular insertion points and compare different variants of a heatmap-based landmark detection pipeline. In this comparison, we demonstrate very likely pitfalls of apparently simple detection and localisation metrics which highlights the importance of a clear detection strategy and the definition of an upper limit for localisation-based metrics. Our preliminary results indicate that a combination of different metrics is necessary, as they yield different winners for method comparison. Additionally, they highlight the need of a comprehensive metric description and evaluation standardisation, especially for the error cases where no metrics could be computed or where no lower/upper boundary of a metric exists. Code and labels: https://github.com/Cardio-AI/rvip_landmark_detection