# Region-of-Interest-Based Cardiac Image Segmentation with Deep Learning
## 6 Conclusions  
In this paper, we presented data on training and testing fundamentally different model architectures, one of which not being explicitly designed for medical image segmentation, DeepLab, and showed that ensembling of such models is beneficial. Starting from an existing idea [27], we optimized training for ROI segmentation in an entirely 2D slice-by-slice manner, obtaining close results to theoretical bests. We are aware that our study has limitations--the size of our acquired pilot dataset is very small. However, we also tested our method on a publicly available, significantly larger dataset, on which we obtain good results, also. Thus, we provide stronger validation for our method. In conclusion, we presented a simple, practical approach to cardiac image segmentation from MRI images, demonstrating that both ensembling of different architectures and using ROI localization improved performance. Moreover, this approach should be applicable to any segmentation problem where an ROI might be present. Our approach provides an opportunity to serve as a building block of a computer-aided diagnostic system in a clinical setting. As future work, we plan to investigate a similar approach in the 3D context and a semi-supervised approach (when in the training set there are some labeled images and some unlabeled images). Furthermore, we plan to integrate the proposed approach in a real system (in this case, some quantisation methods could be required in order to reduce/scale down the learned model). Conceptualization, R.-R.G., A.A. and L.D. ; methodology, R.-R.G. ; software, R.-R.G. ; validation, R.-R.G., A.A., L.D. and Z.B. ; formal analysis, R.-R.G. ; investigation, R.-R.G., S.M. and L.P.; resources, A.A., L.D. and Z.B. ; data curation, A.-A., L.D., S.M., L.P. and Z.B. ; writing--original draft preparation, R.-R.G. ; writing--review and editing, R.-R.G., A.A., L.D., S.M., L.P. and Z.B. ; visualization, R.-R.G. ; supervision, A.A., L.D. and Z.B. ; project administration, Z.B. ; funding acquisition, Z.B. All authors have read and agreed to the published version of the manuscript. The authors highly acknowledge financial support from the Competitiveness Operational Program 2014-2020 POC-A1-A1.1.4-E-2015, financed under the European Regional Development Fund, project number P37_245. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Model** & **Dice RV** & **Dice LV** & **Dice Myo** & **Mean Dice** & **StDev** & **Min–Max** \\ \hline \(M_{1}\): DeepLab & 90.50 & 94.50 & 89.10 & 91.37 & 3.2 & 80.6–95.2 \\ \(M_{3}\): Unet & 89.32 & 95.07 & 89.47 & 91.29 & 3.0 & 83.1–95.5 \\ \(M_{10}\): DeepLab \& DeepLab & 90.58 & 94.72 & 89.53 & 91.61 & 3.4 & 77.5–95.9 \\ \(M_{11}\): DeepLab \& Unet & 90.65 & 95.02 & 89.94 & 91.87 & 3.0 & 81.7–95.7 \\ \(M_{12}\): Standard Ensemble (\(M_{1}\) and \(M_{3}\)) & 90.48 & 95.12 & 89.89 & 91.83 & 3.0 & 82.5–95.9 \\ \(M_{13}\): ROI Ensemble (\(M_{10}\) and \(M_{11}\)) & 90.88 & 94.98 & 90.11 & 91.99 & 3.2 & 80.0–95.9 \\ \(M_{14}\): All Ensemble (\(M_{12}\) and \(M_{13}\)) & 91.26 & 95.39 & 90.49 & 92.38 & 2.9 & 81.7–96.2 \\ \hline \end{tabular}
\end{table} 
Table 4: Results on the ACDC validation set (RV—right ventricle, LV—left ventricle, Myo—myocardium). \begin{table}
\begin{tabular}{c c c c} \hline
**Model** & **Hausdorff RV (mm)** & **Hausdorff LV (mm)** & **Hausdorff Myo (mm)** \\ \hline \(M_{1}\): DeepLab & 24.27 & 11.93 & 14.46 \\ \(M_{14}\): All Ensemble & 13.21 & 10.99 & 12.08 \\ \hline \end{tabular}
\end{table} 
Table 5: Results on the ACDC test set—Hausdorff Distance (RV—right ventricle, LV—left ventricle, Myo—myocardium). **Institutional Review Board Statement:** The study obtained ethical approval from the local Ethics committee (Nr.20117/04.10.2016) and recruited subjects between 2017-2020. **Informed Consent Statement:** All subjects gave their written informed consent to participate in the study. Patients and healthy volunteers underwent cardiological evaluation using ECG and echocardiography, followed by cardiac MRI measurements.