# Unsupervised Image Segmentation on 2D Echocardiogram
## 2 Related Work
### Unsupervised Medical Image Segmentation  
In the field of cardiac image segmentation, several unsupervised methods have been developed to tackle the challenges posed by the lack of labeled datasets. [PERSON] et al. [20] proposed an unsupervised medical image segmentation method, which was evaluated on cardiovascular magnetic resonance (MR) images. This approach demonstrated effectiveness in segmenting the ventricular myocardium and blood pool, achieving a mean Dice score of 0.60 and a cross-validated Dice score of 0.51, without relying on predefined shape models. [PERSON] et al. [11] introduced a method that utilized particle swarm optimization (PSO) combined with active contours and shape priors to segment human hearts and ventricular areas from CT and MR images. This method, designed to handle the high variability in cardiac structures, yielded promising segmentation results, with a Dice score of 0.89, and demonstrated significant improvement over traditional Active Contour Model (ACM) and interactive methods in handling noise and concavities. However, both studies did not utilize echocardiograms in their analyses.  
[PERSON] et al. propose GraphEcho, a graph-driven approach for unsupervised domain adaptation (UDA) tailored to echocardiogram video segmentation [21]. GraphEcho addresses the challenges associated with transferring segmentation models across clinical sites with significant domain shifts, which can negatively impact model performance. The method introduces two key modules: Spatial-wise Cross-domain Graph Matching (SCGM) to align cardiac structures across source and target domains by capturing both local (class-specific) and global (inter-class) relationships, and Temporal Cycle Consistency (TCC), leveraging the cyclical nature of heartbeats to maintain temporal consistency across video frames. Using the newly introduced high-resolution CardiacUDA dataset collected from multiple clinical centers, GraphEcho achieved notable improvements over existing UDA techniques, including enhanced Dice scores on benchmark datasets CAMUS and Echonet Dynamic. These results demonstrate the effectiveness of GraphEcho's graph-matching approach in adapting models to unlabeled target domains while maintaining performance close to models trained directly on target data.  
Further advancements in unsupervised cardiac segmentation include the work by [PERSON] et al. [15], which focused on adapting unsupervised techniques across different imaging modalities, specifically on point clouds and multi-modal cardiac MR images. Their method employed entropy minimization to align features between different modalities, achieving a Dice coefficient of 0.89 for myocardial segmentation. Additionally, a study by [PERSON] et al. (2016) on myocardial segmentation in cardiac MR images also adopted an unsupervised approach, demonstrating robustness to noise and achieving Dice scores up to 0.91 when compared to manual annotations. Neither study employed echocardiograms, focusing instead on MR and other imaging modalities. These studies highlight the potential of unsupervised techniques in cardiology, particularly in overcoming the challenge of limited labeled datasets, and demonstrate comparable performance to supervised methods in terms of segmentation accuracy but using images with higher quality when compared to echocardiograms.