# Unsupervised Image Segmentation on 2D Echocardiogram
## 2 Related Work
### Deep Learning for Image Segmentation in Medical Image Analysis  
Image segmentation, which partitions an image into meaningful regions representing different anatomical structures or abnormalities, is fundamental in medical image analysis [8; 9]. It plays a critical role in extracting quantitative information, visualizing anatomical structures, and guiding image-guided interventions.  
Segmentation enables the quantification of anatomical features such as organ volume, wall thickness, and lesion size, providing essential data for diagnosis, treatment planning, and disease monitoring [9]. Deep learning models provide segmentation masks that allow for the creation of detailed 3D visualizations of anatomical structures, facilitating surgical planning and improving patient education. For instance, [PERSON] et al. [10] explored segmentation in image-guided surgery (IGS) by leveraging deep learning techniques for generating 3D visualizations of patient anatomy, which were used for preoperative planning and intraoperative navigation. Their study showed significant improvements in surgical accuracy and outcomes through the use of augmented reality (AR) and virtual reality (VR) visualization technologies.  
In another study, [PERSON] et al. [11] applied unsupervised segmentation techniques to cardiac images, focusing on myocardial and blood pool segmentation in cardiac MR images. Their approach, which combined swarm optimization with active contours, achieved high segmentation accuracy with a Dice score of 0.89 and provided a robust framework for handling noisy datasets. The Dice score is a commonly used metric in image segmentation, which measures the overlap between the predicted segmentation and the ground truth, with a score of 1 indicating perfect overlap and 0 indicating no overlap. These methods highlight the potential of deep learning and unsupervised techniques in advancing the field of cardiology by improving image segmentation and visualization capabilities for better clinical decision-making.  
Additionally, real-time segmentation can assist in guiding minimally invasive procedures, enabling surgeons to accurately target instruments while avoiding critical structures. For instance, [PERSON] et al. [12] proposed a novel endoscope automation framework using real-time segmentation and tracking during minimally invasive surgery. Their system integrated hierarchical quadratic programming (HQP) control with interactive perception modules, utilizing a deep CNN to segment the surgical scene and track multi-tools in real-time. This framework significantly improved precision, with minimal visual misalignment errors during tissue resection tasks. Another study by [PERSON] et al. [13] focused on needle-based interventions, employing a machine learning model based on a U-Net architecture to perform 3D ultrasound segmentation. This allowed for the accurate targeting of biopsy needles while avoiding critical anatomical structures, improving safety and precision during procedures. Both approaches underscore the potential of real-time segmentation in enhancing surgical outcomes through improved visual guidance and instrument tracking [8].