# An Improved Approach for Cardiac MRI Segmentation based on 3D UNet Combined with Papillary Muscle Exclusion

arXiv: https://arxiv.org/abs/2410.06818 · HTML: https://mlsanity.com/paper/2410.06818/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 85 references that are in the map.
- First posted on arXiv 9 Oct 2024.

## Builds on (most cited first, 11 of 11)

- [Multivariate mixture model for myocardium segmentation combining multi-source images](https://mlsanity.com/paper/1612.08820/) (2016)
- [Deep learning for cardiac image segmentation: A review](https://mlsanity.com/paper/1911.03723/) (2019)
- [Cardiac Segmentation with Strong Anatomical Guarantees](https://mlsanity.com/paper/2006.08825/) (2020)
- [MyoPS-Net: Myocardial Pathology Segmentation with Flexible Combination of Multi-Sequence CMR Images](https://mlsanity.com/paper/2211.03062/) (2022)
- [A new method incorporating deep learning with shape priors for left ventricular segmentation in myocardial perfusion SPECT images](https://mlsanity.com/paper/2206.03603/) (2022)
- [Automatic Myocardial Segmentation by Using A Deep Learning Network in Cardiac MRI](https://mlsanity.com/paper/1708.07452/) (2017)
- [Left Ventricle Segmentation in Cardiac MR Images Using Fully Convolutional Network](https://mlsanity.com/paper/1802.07778/) (2018)
- [Left Ventricle Segmentation and Quantification from Cardiac Cine MR Images via Multi-task Learning](https://mlsanity.com/paper/1809.10221/) (2018)
- [CondenseUNet: A Memory-Efficient Condensely-Connected Architecture for Bi-ventricular Blood Pool and Myocardium Segmentation](https://mlsanity.com/paper/2004.02249/) (2020)
- [Deep Learning from Dual-Energy Information for Whole-Heart Segmentation in Dual-Energy and Single-Energy Non-Contrast-Enhanced Cardiac CT](https://mlsanity.com/paper/2008.03985/) (2020)
- [Automatic Segmentation of Left Ventricle in Cardiac Magnetic Resonance Images](https://mlsanity.com/paper/2201.12805/) (2022)

## Built on by (most cited first, 1 of 1)

- [SV-Cine: Diagnosis-Conditioned Segmentation of Single Ventricle Physiology via Generative Data Augmentation](https://mlsanity.com/paper/2609.12997/) (2026)

## Abstract (arXiv)

Left ventricular ejection fraction (LVEF) is the most important clinical parameter of cardiovascular function. The accuracy in estimating this parameter is highly dependent upon the precise segmentation of the left ventricle (LV) structure at the end diastole and systole phases. Therefore, it is crucial to develop robust algorithms for the precise segmentation of the heart structure during different phases. Methodology: In this work, an improved 3D UNet model is introduced to segment the myocardium and LV, while excluding papillary muscles, as per the recommendation of the Society for Cardiovascular Magnetic Resonance. For the practical testing of the proposed framework, a total of 8,400 cardiac MRI images were collected and analysed from the military hospital in Tunis (HMPIT), as well as the popular ACDC public dataset. As performance metrics, we used the Dice coefficient and the F1 score for validation/testing of the LV and the myocardium segmentation. Results: The data was split into 70%, 10%, and 20% for training, validation, and testing, respectively. It is worth noting that the proposed segmentation model was tested across three axis views: basal, medio basal and apical at two different cardiac phases: end diastole and end systole instances. The experimental results showed a Dice index of 0.965 and 0.945, and an F1 score of 0.801 and 0.799, at the end diastolic and systolic phases, respectively. Additionally, clinical evaluation outcomes revealed a significant difference in the LVEF and other clinical parameters when the papillary muscles were included or excluded.