## 1 Introduction  
Meniscus tissue is one of the soft tissues in the human joint located between the femur and tibia in the knee joint. The meniscus comprises C-shaped fibro-cartilaginous tissue and consists of the lateral and medial meniscus. These structures have essential functions: providing joint stability, shock absorption, joint fluid distribution, and load transfer [1, 2]. Meniscal injuries usually lead to changes in joint biomechanics that affect load distribution and contact stresses [3]. Meniscal injuries are common knee injuries that can be seen in all age groups and usually occur as a result of sporting activities, aging-related degeneration, or traumatic effects. Accurate diagnosis of meniscal injuries is essential to determine the type and extent of damage. Magnetic resonance imaging (MRI) is the gold standard for non-invasive diagnosis. MRI is widely used in the preliminary diagnosis of knee injuries due to its noninvasive properties and exceptional ability to provide clear visualization of soft tissue (high-contrast resolution) [4]. This method, which assesses the structural integrity of meniscal tissue with high precision, is often time-consuming and requires expert interpretation. In recent years,several studies have been conducted using deep learning (DL) algorithms to detect and evaluate meniscal injuries on MRI images [5; 6]. Meniscal segmentation automatically or manually determines the boundaries of meniscal tissue and separates it from surrounding tissues [7; 8]. Segmentation is usually performed using pixel-based or region-based methods. With the rise of deep learning, image segmentation has been divided into three main categories: (i) semantic segmentation, (ii) instance segmentation, and (iii) panoptic segmentation. Semantic segmentation assigns each pixel in an image to a specific class, while instance segmentation separately identifies different objects belonging to the same class. Panoptic segmentation combines these approaches by labeling each pixel as an object or background [9; 10]. Some DL-based studies for detecting and classifying meniscal injuries support this process by segmenting meniscal tissue [4; 11]. Segmentation is not only used in injury detection, but also in treatment planning and surgical interventions. In particular, meniscal segmentation from knee MRI images plays an important role in analyzing the length, width, height, cross-sectional area, and surface area of the meniscus for meniscal allograft transplantation using a 3D reconstruction model based on the patient's normal meniscus [12]. The segmentation process can be complicated because MRI images have multiple sequences showing different tissues at various intensity and contrast levels. This causes structures with no clear boundaries, such as the meniscus, to appear in different shapes and intensities in each sequence. However, DL-based segmentation methods offer a more efficient approach to overcoming these challenges than traditional methods by providing high accuracy, speed, and automation advantages. As a result, these innovative methods provide a versatile contribution to the detection and treatment of meniscal injuries. In recent years, studies on DL-based meniscus tissue segmentation have increased and differed in imaging methods, segmentation architectures, and targeted tasks. Although most of the studies in the literature are based on MRI images, alternative methods such as ultrasonography are also used. Especially for knee osteoarthritis (KOA), where early diagnosis is difficult and evaluation methods are limited, ultrasound stands out as a simple, economical, and non-invasive tool. Ultrasonography offers a powerful alternative to traditional methods by providing fast and effective automated segmentation in meniscal assessment. In this context, segmentation models developed with deep learning techniques achieve successful results in tasks such as the analysis of ultrasound images and automatic measurement of the meniscal area [13].