# Effect of Data Augmentation on Deep-Learning-Based Segmentation of Long-Axis Cine-MRI
## 6 Conclusions  
In this work, we present an automated segmentation framework for the detection of anatomical structures on Cine-MRI LAX images, which exhibit more complex structures compared to the SAX orientation. We assess the effect of a comprehensive hierarchical data-augmentation strategy in detecting LAX anatomical structures, demonstrating its ability to produce robust results against anatomical anomalies and image degradation. The strategy has also shown its usefulness in producing acceptable segmentation outputs to compute the LVEF clinical metric, with Bland\(-\)Altman plots displaying a negligible bias over the four-chamber test set.  
We demonstrate that the ENet CNN architecture is a viable option for whole-heart segmentation in both two- and four-chamber sequences, achieving acceptable results. The  
Figure 10: Example of typical errors produced by ENet architecture compared to ground truth and UNet prediction. A small overlap outside of the real myocardium is visible. The same color scheme from Figure 9 is applied.  
combination of an acceptable performance in both orientations along with the compact size of ENet makes this model attractive for practical real-time applications and deployment. While the segmentation may be less precise near anatomical frontiers with small overlaps, the obtained results make its use for clinical purposes feasible.  
We also conducted a short comparison with a barebone UNet architecture, revealing slightly lower performances depending on the dataset. While it can be hypothesized that core conceptual differences come into play regarding the observed behaviors, this comparison would benefit from additional experiments to observe better characterization and highlight differences between the outputs produced by the two architectures.  
Conceptualization, R.M. and T.D.; methodology, R.M. and F.L.; software, F.L.; validation, R.M., A.L. and T.D.; formal analysis, F.L.; investigation, F.L.; resources, A.L.; data curation, F.L. and L.M.; writing--original draft preparation, F.L.; writing--review and editing, R.M., A.L. and T.D.; visualization, F.L.; supervision, R.M., A.L. and T.D.; project administration, A.L.; funding acquisition, A.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the French National Research Agency (ANR), under grant number ANR-21-PRRD-0047-01.  
The DETERMINE LV Segmentation Challenge of the Cardiac Atlas Project database is available upon request at [[https://www.cardiac.org/lv-segmentation-challenge/](https://www.cardiac.org/lv-segmentation-challenge/)]([https://www.cardiac.org/lv-segmentation-challenge/](https://www.cardiac.org/lv-segmentation-challenge/)) (accessed on 26 October 2021). Its authorization follows the rules according to the document [[https://cardiac.sproject.isd.kcl.ac.uk/wp-content/uploads/2022/10/CONSENSUS-AUTO-TERMS.pdf](https://cardiac.sproject.isd.kcl.ac.uk/wp-content/uploads/2022/10/CONSENSUS-AUTO-TERMS.pdf)]([https://cardiac.sproject.isd.kcl.ac.uk/wp-content/uploads/2022/10/CONSENSUS-AUTO-TERMS.pdf](https://cardiac.sproject.isd.kcl.ac.uk/wp-content/uploads/2022/10/CONSENSUS-AUTO-TERMS.pdf)). The MnM2 challenge database is publicly available at [[https://www.ub.edu/mms-2/](https://www.ub.edu/mms-2/)]([https://www.ub.edu/mms-2/](https://www.ub.edu/mms-2/)) (accessed on 1 March 2022). The authors are not allowed to share the data collected at CHU-Djon Hospital.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CVD & Cardio Vascular Diseases \\ SAX & Short axis \\ LAX & Long axis \\ CMRI & Cardiac Magnetic Resonance Imaging \\ ED & End diastole \\ ES & End systole \\ LVEF & Left Ventricular Ejection Fraction \\ LVC & Left ventricle cavity \\ MYO & Myocardium \\ LA & Left atrium \\ RV & Right ventricle \\ RA & Right atrium \\ AO & Aorta \\ \end{tabular}