# Unsupervised Image Segmentation on 2D Echocardiogram
## 1 Introduction  
Medical imaging plays a pivotal role in the diagnosis, treatment, and monitoring of various medical conditions. Artificial intelligence (AI) is transforming the healthcare landscape, and its application in cardiology is particularly promising [1]. In echocardiography--a type of ultrasound used to examine the heart--AI is being utilized [2] to automate tasks, improve accuracy, and potentially reduce costs. Among the most commonly used methods in cardiology, 2D echocardiography stands out due to its non-invasive nature, real-time imaging capabilities, and widespread availability in clinical settings. Echocardiography uses ultrasound waves to create detailed images of the heart, enabling clinicians to assess cardiac function, structure, and pathology. However, despite its significant advantages, the interpretation of echocardiograms remains a challenging task, often requiring expert knowledge and considerable manual effort [3]. The complexity of cardiac structures, coupled with the variability and noise inherent in echocardiographic images, makes segmentation a critical yet difficult task. Image segmentation, the process of partitioning an image into meaningful regions, is a fundamental pre-processing step that facilitates subsequent analysis and diagnosis. Traditional segmentation methods, such as normalized cuts and graph-based techniques, rely on predefinedfeatures, which often fail to capture the complexity of medical images [4]. These methods are particularly ill-suited for echocardiograms, where consistent features are difficult to define due to variability in imaging conditions, patient anatomy, and pathology. In recent years, deep learning, particularly convolutional neural networks (CNNs), has revolutionized medical image analysis, offering automated, accurate, and efficient solutions for segmentation tasks. CNNs have demonstrated remarkable success in supervised image segmentation by learning hierarchical features from large, annotated datasets. Notable supervised approaches, such as fully convolutional networks (FCNs), U-Net, and SegNet, have been widely adopted for various medical imaging applications, including cardiac imaging, brain segmentation, and retinal vessel detection [3; 4]. These models excel at learning complex patterns from labeled data, enabling them to achieve state-of-the-art performance on challenging medical images. However, supervised learning has its limitations. The primary challenge lies in the need for extensive labeled data, which is often scarce and expensive to obtain, particularly in the medical domain. In echocardiography, where expert knowledge is required to manually annotate cardiac structures, acquiring labeled datasets is both time-consuming and resource-intensive. Furthermore, annotated datasets may not capture the full diversity of imaging conditions, leading to a reduced generalization of supervised models in real-world clinical settings [5]. These limitations highlight the need for alternative approaches that can operate effectively without relying on large, annotated datasets. In response to these challenges, there has been growing interest in unsupervised image segmentation methods, which aim to identify meaningful structures within an image based solely on the intrinsic properties of the data, without the need for labeled training data [5]. These methods offer a promising alternative, particularly in medical domains where labeled data are limited. Several unsupervised methodologies have been proposed for image segmentation, including k-means clustering, Gaussian mixture models (GMMs), and self-organizing maps (SOMs). While these approaches offer simplicity and effectiveness in some scenarios, they often struggle with the complexity and variability of medical images, particularly echocardiograms. Recent advancements in unsupervised learning have focused on integrating CNNs with unsupervised learning objectives, combining the representational power of deep networks with clustering and feature learning techniques. For instance, [PERSON] et al.