# A new method incorporating deep learning with shape priors for left ventricular segmentation in myocardial perfusion SPECT images

arXiv: https://arxiv.org/abs/2206.03603 · HTML: https://mlsanity.com/paper/2206.03603/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 45 references that are in the map.
- First posted on arXiv 7 Jun 2022.

## Builds on (most cited first, 5 of 5)

- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [Deep learning for cardiac image segmentation: A review](https://mlsanity.com/paper/1911.03723/) (2019)
- [Fully Convolutional Multi-scale Residual DenseNets for Cardiac Segmentation and Automated Cardiac Diagnosis using Ensemble of Classifiers](https://mlsanity.com/paper/1801.05173/) (2018)
- [Shape constrained CNN for segmentation guided prediction of myocardial shape and pose parameters in cardiac MRI](https://mlsanity.com/paper/2203.01089/) (2022)

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

- [An Improved Approach for Cardiac MRI Segmentation based on 3D UNet Combined with Papillary Muscle Exclusion](https://mlsanity.com/paper/2410.06818/) (2024)
- [Leveraging Anatomical Priors for Automated Pancreas Segmentation on Abdominal CT](https://mlsanity.com/paper/2504.06921/) (2025)
- [Continuous max-flow augmentation of self-supervised few-shot learning on SPECT left ventricles](https://mlsanity.com/paper/2405.05520/) (2024)
- [FedDA-TSformer: Federated Domain Adaptation with Vision TimeSformer for Left Ventricle Segmentation on Gated Myocardial Perfusion SPECT Image](https://mlsanity.com/paper/2502.16709/) (2025)
- [Diagnosis of Pulmonary Hypertension by Integrating Multimodal Data with a Hybrid Graph Convolutional and Transformer Network](https://mlsanity.com/paper/2504.01025/) (2025)
- [Compute-Optimal Network Design for Echocardiography Myocardial Segmentation and Perfusion Quantification using Neural Scaling Laws](https://mlsanity.com/paper/2606.06725/) (2026)

## Abstract (arXiv)

Background: The assessment of left ventricular (LV) function by myocardial perfusion SPECT (MPS) relies on accurate myocardial segmentation. The purpose of this paper is to develop and validate a new method incorporating deep learning with shape priors to accurately extract the LV myocardium for automatic measurement of LV functional parameters. Methods: A segmentation architecture that integrates a three-dimensional (3D) V-Net with a shape deformation module was developed. Using the shape priors generated by a dynamic programming (DP) algorithm, the model output was then constrained and guided during the model training for quick convergence and improved performance. A stratified 5-fold cross-validation was used to train and validate our models. Results: Results of our proposed method agree well with those from the ground truth. Our proposed model achieved a Dice similarity coefficient (DSC) of 0.9573(0.0244), 0.9821(0.0137), and 0.9903(0.0041), a Hausdorff distances (HD) of 6.7529(2.7334) mm, 7.2507(3.1952) mm, and 7.6121(3.0134) mm in extracting the endocardium, myocardium, and epicardium, respectively. Conclusion: Our proposed method achieved a high accuracy in extracting LV myocardial contours and assessing LV function.