# Learning Shape Priors for Robust Cardiac MR Segmentation from Multi-view Images

arXiv: https://arxiv.org/abs/1907.09983 · HTML: https://mlsanity.com/paper/1907.09983/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2019.

## Built on by (most cited first, 16 of 16)

- [Multi-Modality Cardiac Image Computing: A Survey](https://mlsanity.com/paper/2208.12881/) (2022)
- [MulViMotion: Shape-aware 3D Myocardial Motion Tracking from Multi-View Cardiac MRI](https://mlsanity.com/paper/2208.00034/) (2022)
- [Orthogonal Annotation Benefits Barely-supervised Medical Image Segmentation](https://mlsanity.com/paper/2303.13090/) (2023)
- [Towards Cardiac MRI Foundation Models: Comprehensive Visual-Tabular Representations for Whole-Heart Assessment and Beyond](https://mlsanity.com/paper/2504.13037/) (2025)
- [SADIR: Shape-Aware Diffusion Models for 3D Image Reconstruction](https://mlsanity.com/paper/2309.03335/) (2023)
- [Right Ventricular Segmentation from Short- and Long-Axis MRIs via Information Transition](https://mlsanity.com/paper/2109.02171/) (2021)
- [Whole Heart 3D+T Representation Learning Through Sparse 2D Cardiac MR Images](https://mlsanity.com/paper/2406.00329/) (2024)
- [Learning With Context Feedback Loop for Robust Medical Image Segmentation](https://mlsanity.com/paper/2103.02844/) (2021)
- [Tempera: Spatial Transformer Feature Pyramid Network for Cardiac MRI Segmentation](https://mlsanity.com/paper/2203.00355/) (2022)
- [GL-Fusion: Global-Local Fusion Network for Multi-view Echocardiogram Video Segmentation](https://mlsanity.com/paper/2309.11144/) (2023)
- [Shape constrained CNN for segmentation guided prediction of myocardial shape and pose parameters in cardiac MRI](https://mlsanity.com/paper/2203.01089/) (2022)
- [Slice Imputation: Intermediate Slice Interpolation for Anisotropic 3D Medical Image Segmentation](https://mlsanity.com/paper/2203.10773/) (2022)
- [Automatic view plane prescription for cardiac magnetic resonance imaging via supervision by spatial relationship between views](https://mlsanity.com/paper/2309.12805/) (2023)
- [NISF++: Geometrically-grounded implicit representations of 3D+time cardiac function from 2D short- and long-axis MR views](https://mlsanity.com/paper/2608.00752/) (2026)
- [Multi-Disease-Aware Training Strategy for Cardiac MR Image Segmentation](https://mlsanity.com/paper/2503.17896/) (2025)
- [Convolutional Feature Noise Reduction for 2D Cardiac MR Image Segmentation](https://mlsanity.com/paper/2511.22983/) (2025)

## Abstract (arXiv)

Cardiac MR image segmentation is essential for the morphological and functional analysis of the heart. Inspired by how experienced clinicians assess the cardiac morphology and function across multiple standard views (i.e. long- and short-axis views), we propose a novel approach which learns anatomical shape priors across different 2D standard views and leverages these priors to segment the left ventricular (LV) myocardium from short-axis MR image stacks. The proposed segmentation method has the advantage of being a 2D network but at the same time incorporates spatial context from multiple, complementary views that span a 3D space. Our method achieves accurate and robust segmentation of the myocardium across different short-axis slices (from apex to base), outperforming baseline models (e.g. 2D U-Net, 3D U-Net) while achieving higher data efficiency. Compared to the 2D U-Net, the proposed method reduces the mean Hausdorff distance (mm) from 3.24 to 2.49 on the apical slices, from 2.34 to 2.09 on the middle slices and from 3.62 to 2.76 on the basal slices on the test set, when only 10% of the training data was used.