# Unsupervised Image Segmentation on 2D Echocardiogram
## 5 Conclusions  
In this study, we trained and evaluated an unsupervised model for echocardiogram segmentation, focusing on the left ventricle for metric assessments. One of the main challenges of training an unsupervised model lies in the absence of labeled data, but this limitation also serves as an advantage. With the support of quality assessment methods, which ensure the collection of high-quality data even without labels, the model can achieve comparable or even superior performance. This is a significant advantage over supervised methods, as the model can operate in environments where labeled data are limited or unavailable. The flexibility of unsupervised models allows for broader applications, and the results of our model showcase this potential. The simplicity of our model's architecture, based on a U-Net with 22 convolutional layers, offers a distinct advantage compared to EchoNet-Dynamic, which employs a Deeplabv3 architecture with a 50-layer residual network for segmentation. Furthermore, EchoNet-Dynamic leverages pre-trained R2+1D spatio-temporal convolutions from the Kinetics-400 dataset, while our model was trained from scratch without pre-trained initialization. This highlights a key strength of unsupervised models, which do not rely on pre-existing labeled datasets, yet can still perform well when provided with high-quality training data and a robust architecture. Another major difference between our model and EchoNet-Dynamic lies in the dataset size. EchoNet-Dynamic was trained on a large dataset of 7465 videos with pre-processed labels and segmentation masks, whereas our model demonstrated competitive results with a significantly smaller dataset. This ability to train effectively with fewer data points presents a substantial advantage in settings where acquiring medical data is costly and time-consuming, and labeled data are even more difficult to obtain and ensure precision. Our model showed competitive segmentation accuracy and ejection fraction calculation when compared to EchoNet-Dynamic in the same testing environment. The pixel-level precision achieved by our network is particularly useful in applications requiring detailed boundary detection, though it presents challenges in noisy environments where artifacts may cause incorrect segmentations. This underscores the importance of integrating robust data quality assessments during the training process. Looking forward, there are several paths for improvement and future research. While our unsupervised model performed well, integrating semi-supervised or weakly supervised learning approaches could further enhance its capabilities by leveraging small amounts of labeled data to fine-tune performance. Additionally, expanding the dataset used for training, including more diverse echocardiographic images and patient conditions, could improve generalizability and robustness. Exploring more advanced post-processing techniques could also help reduce the impact of noise and artifacts in real-world clinical settings. By continuing to refine these techniques, unsupervised models can offer increasingly accurate and scalable solutions for echocardiogram analysis, contributing to more efficient and accessible cardiac care. Conceptualization, G.F.C. and D.D. ; methodology, G.F.C. and D.D. ; formal analysis, G.F.C. and D.D. ; investigation, G.F.C. and D.D. ; writing--original draft preparation, G.F.C. ; writing--review and editing, G.F.C., D.D. and N.N. ; supervision, D.D. and N.N. ; project administration, D.D. All authors have read and agreed to the published version of the manuscript. This research received no external funding.