# Optimized Automated Cardiac MR Scar Quantification with GAN-Based Data Augmentation

arXiv: https://arxiv.org/abs/2109.12940 · HTML: https://mlsanity.com/paper/2109.12940/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 33 references that are in the map.
- First posted on arXiv 27 Sep 2021.

## Builds on (most cited first, 12 of 12)

- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019)
- [GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification](https://mlsanity.com/paper/1803.01229/) (2018)
- [Deep learning for cardiac image segmentation: A review](https://mlsanity.com/paper/1911.03723/) (2019)
- [GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks](https://mlsanity.com/paper/1810.10863/) (2018)
- [Deep Learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge](https://mlsanity.com/paper/2108.04016/) (2021)
- [Cascaded Convolutional Neural Network for Automatic Myocardial Infarction Segmentation from Delayed-Enhancement Cardiac MRI](https://mlsanity.com/paper/2012.14128/) (2020)
- [XCAT-GAN for Synthesizing 3D Consistent Labeled Cardiac MR Images on Anatomically Variable XCAT Phantoms](https://mlsanity.com/paper/2007.13408/) (2020)
- [4D Semantic Cardiac Magnetic Resonance Image Synthesis on XCAT Anatomical Model](https://mlsanity.com/paper/2002.07089/) (2020)
- [Fully Automated Myocardial Strain Estimation from CMR Tagged Images using a Deep Learning Framework in the UK Biobank](https://mlsanity.com/paper/2004.07064/) (2020)
- [Cascaded Framework for Automatic Evaluation of Myocardial Infarction from Delayed-Enhancement Cardiac MRI](https://mlsanity.com/paper/2012.14556/) (2020)
- [Automatic Myocardial Disease Prediction From Delayed-Enhancement Cardiac MRI and Clinical Information](https://mlsanity.com/paper/2010.08469/) (2020)
- [CardiSort: a convolutional neural network for cross vendor automated sorting of cardiac MR images](https://mlsanity.com/paper/2109.08479/) (2021)

## Built on by (most cited first, 5 of 5)

- [Generative AI for Synthetic Data Across Multiple Medical Modalities: A Systematic Review of Recent Developments and Challenges](https://mlsanity.com/paper/2407.00116/) (2024)
- [Pathology Synthesis of 3D-Consistent Cardiac MR Images using 2D VAEs and GANs](https://mlsanity.com/paper/2209.04223/) (2022)
- [A Data Augmentation Pipeline to Generate Synthetic Labeled Datasets of 3D Echocardiography Images using a GAN](https://mlsanity.com/paper/2403.05384/) (2024)
- [Error correcting 2D-3D cascaded network for myocardial infarct scar segmentation on late gadolinium enhancement cardiac magnetic resonance images](https://mlsanity.com/paper/2306.14725/) (2023)
- [3D Conditional Image Synthesis of Left Atrial LGE MRI from Composite Semantic Masks](https://mlsanity.com/paper/2601.04588/) (2026)

## Abstract (arXiv)

Background: The clinical utility of late gadolinium enhancement (LGE) cardiac MRI is limited by the lack of standardization, and time-consuming postprocessing. In this work, we tested the hypothesis that a cascaded deep learning pipeline trained with augmentation by synthetically generated data would improve model accuracy and robustness for automated scar quantification. Methods: A cascaded pipeline consisting of three consecutive neural networks is proposed, starting with a bounding box regression network to identify a region of interest around the left ventricular (LV) myocardium. Two further nnU-Net models are then used to segment the myocardium and, if present, scar. The models were trained on the data from the EMIDEC challenge, supplemented with an extensive synthetic dataset generated with a conditional GAN. Results: The cascaded pipeline significantly outperformed a single nnU-Net directly segmenting both the myocardium (mean Dice similarity coefficient (DSC) (standard deviation (SD)): 0.84 (0.09) vs 0.63 (0.20), p < 0.01) and scar (DSC: 0.72 (0.34) vs 0.46 (0.39), p < 0.01) on a per-slice level. The inclusion of the synthetic data as data augmentation during training improved the scar segmentation DSC by 0.06 (p < 0.01). The mean DSC per-subject on the challenge test set, for the cascaded pipeline augmented by synthetic generated data, was 0.86 (0.03) and 0.67 (0.29) for myocardium and scar, respectively. Conclusion: A cascaded deep learning-based pipeline trained with augmentation by synthetically generated data leads to myocardium and scar segmentations that are similar to the manual operator, and outperforms direct segmentation without the synthetic images.