# Unsupervised Image Segmentation on 2D Echocardiogram
## 2 Related Work
### Deep Learning in Echocardiography  
Deep learning's ability to analyze large datasets and learn complex patterns has made it highly suitable for echocardiogram analysis [2]. Various applications have emerged in this domain, including automated view classification, quantitative cardiac function assessment, direct disease detection, and automated image quality assessment. CNNs have demonstrated remarkable accuracy in classifying standard echocardiographic views such as apical four-chamber and parasternal long-axis views [14]. Automated classification is crucial for efficient analysis, as manual classification can be time-consuming and prone to inconsistencies [3; 14]. For instance, one study leveraged CNNs to classify 23 different echocardiographic viewpoints with high accuracy [5].  
Additionally, deep learning models are increasingly being used to quantitatively assess cardiac function by automating the measurement of cardiac parameters, such as ejection fraction (EF), left atrial end-systolic volume (LAESV), and the E/e' ratio, all of which are vital indicators of cardiac health [1]. One of the most recent works includes EchoNet-Dynamic, which leverages temporal information from echocardiogram videos to provide beat-to-beat assessments of cardiac function, including the End-Diastolic Volume (EDV) and End-Systolic Volume (ESV), overcoming the limitations of still-image-based models [7].  
Recent advances in semi-supervised learning have enabled deep learning models to leverage both labeled and unlabeled data, which is particularly valuable for echocardiography where labeled datasets are often limited. By combining labeled and unlabeled data, semi-supervised approaches enhance the model's ability to generalize across diverse cardiac structures, even with limited annotations. [PERSON] and [PERSON] propose a semi-supervised method that integrates both frequency-domain and spatial-domain data augmentations within the Mean Teacher framework [17]. Their approach addresses the challenge of limited annotated data by leveraging unannotated images alongside annotated ones, enhancing the model's ability to capture global and local cardiac features. Specifically, they introduce a frequency-domain mixing strategy, which operates in the frequency domain to increase data diversity while preserving spatial structures, and an image puzzle mixing strategy, which enhances local anatomical feature perception by partitioning images and recombining them in a puzzle-like manner. Evaluated on CAMUS and EchoNet datasets, this method demonstrates superior segmentation accuracy, particularly with limited labeled data, outperforming other semi-supervised methods on metrics such as Dice and Jaccard similarity coefficients.  
Furthermore, studies have explored the potential for deep learning to directly detect cardiac diseases such as hypertrophic cardiomyopathy, cardiac amyloid, and pulmonary hypertension from echocardiograms [2]. Although promising, these studies require further validation before their clinical utility can be confirmed. Moreover, deep learning models are also developed to assess echocardiographic image quality [18]. Quality assessment is crucial for reducing diagnostic errors and enhancing clinical workflow efficiency [14; 19]. One study demonstrated the feasibility of CNNs in evaluating the quality of apical four-chamber echocardiograms [18].