# Aortic root landmark localization with optimal transport loss for heatmap regression

arXiv: https://arxiv.org/abs/2407.04921 · HTML: https://mlsanity.com/paper/2407.04921/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 32 of its 95 references that are in the map.
- First posted on arXiv 6 Jul 2024.

## Builds on (most cited first, 30 of 32)

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- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
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- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Computational Optimal Transport](https://mlsanity.com/paper/1803.00567/) (2018)
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- [MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation](https://mlsanity.com/paper/1902.04049/) (2019)
- [Rethinking the Heatmap Regression for Bottom-up Human Pose Estimation](https://mlsanity.com/paper/2012.15175/) (2020)
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- [Deep Learning-Based Regression and Classification for Automatic Landmark Localization in Medical Images](https://mlsanity.com/paper/2007.05295/) (2020)
- [Modality specific U-Net variants for biomedical image segmentation: A survey](https://mlsanity.com/paper/2107.04537/) (2021)
- [The Wasserstein-Fourier Distance for Stationary Time Series](https://mlsanity.com/paper/1912.05509/) (2019)
- [KNEEL: Knee Anatomical Landmark Localization Using Hourglass Networks](https://mlsanity.com/paper/1907.12237/) (2019)
- [Sharp U-Net: Depthwise Convolutional Network for Biomedical Image Segmentation](https://mlsanity.com/paper/2107.12461/) (2021)
- [Volumetric landmark detection with a multi-scale shift equivariant neural network](https://mlsanity.com/paper/2003.01639/) (2020)
- [Unsupervised Landmark Detection Based Spatiotemporal Motion Estimation for 4D Dynamic Medical Images](https://mlsanity.com/paper/2109.14805/) (2021)
- [WOOD: Wasserstein-based Out-of-Distribution Detection](https://mlsanity.com/paper/2112.06384/) (2021)
- [Partial Policy-based Reinforcement Learning for Anatomical Landmark Localization in 3D Medical Images](https://mlsanity.com/paper/1807.02908/) (2018)
- [Contour Transformer Network for One-shot Segmentation of Anatomical Structures](https://mlsanity.com/paper/2012.01480/) (2020)
- [Learn Fine-grained Adaptive Loss for Multiple Anatomical Landmark Detection in Medical Images](https://mlsanity.com/paper/2105.09124/) (2021)
- [Uniform-in-Time Wasserstein Stability Bounds for (Noisy) Stochastic Gradient Descent](https://mlsanity.com/paper/2305.12056/) (2023)
- [Region Proposal Network with Graph Prior and IoU-Balance Loss for Landmark Detection in 3D Ultrasound](https://mlsanity.com/paper/2004.00207/) (2020)

## Abstract (arXiv)

Anatomical landmark localization is gaining attention to ease the burden on physicians. Focusing on aortic root landmark localization, the three hinge points of the aortic valve can reduce the burden by automatically determining the valve size required for transcatheter aortic valve implantation surgery. Existing methods for landmark prediction of the aortic root mainly use time-consuming two-step estimation methods. We propose a highly accurate one-step landmark localization method from even coarse images. The proposed method uses an optimal transport loss to break the trade-off between prediction precision and learning stability in conventional heatmap regression methods. We apply the proposed method to the 3D CT image dataset collected at Sendai Kousei Hospital and show that it significantly improves the estimation error over existing methods and other loss functions. Our code is available on GitHub.