mlsanityData to 14 Oct 2026

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

arXiv 2206.03603 ↗First posted 7 Jun 2022eess.IV cs.CVcited by 6

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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.

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

5 of 45 references.

References · 5

  1. 2015Spatial Transformer Networkscited by 1,429
  2. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 959
  3. 2019Deep learning for cardiac image segmentation: A reviewcited by 131
  4. 2018Fully Convolutional Multi-scale Residual DenseNets for Cardiac Segmentation and Automated Cardiac Diagnosis using Ensemble of Classifierscited by 35
  5. 2022Shape constrained CNN for segmentation guided prediction of myocardial shape and pose parameters in cardiac MRIcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 1.

6 papers.

  1. 2024An Improved Approach for Cardiac MRI Segmentation based on 3D UNet Combined with Papillary Muscle Exclusioncited by 1
  2. 2025Leveraging Anatomical Priors for Automated Pancreas Segmentation on Abdominal CTcited by 1
  3. 2024Continuous max-flow augmentation of self-supervised few-shot learning on SPECT left ventriclescited by 0
  4. 2025FedDA-TSformer: Federated Domain Adaptation with Vision TimeSformer for Left Ventricle Segmentation on Gated Myocardial Perfusion SPECT Imagecited by 0
  5. 2025Diagnosis of Pulmonary Hypertension by Integrating Multimodal Data with a Hybrid Graph Convolutional and Transformer Networkcited by 0
  6. 2026Compute-Optimal Network Design for Echocardiography Myocardial Segmentation and Perfusion Quantification using Neural Scaling Lawscited by 0