# Advanced Brain Tumor Segmentation Using SAM2-UNet
## 1 Introduction  
Image segmentation is very crucial in the medical science industry, as the accurate and detailed interpretation of data is very important for diagnosis, treatment planning, and monitoring patients' condition, as discussed in [1]. Through segmentation, health care professionals can understand the structure of organs, tissues, or any anomalies if present; this prior analysis would help them to plan the medication needed for the patient and to diagnose them [2]. This segmentation ability is very crucial in fields like neurology, where the precise identification of tumor boundaries is needed to assess the growth of the tumor and the adverse effects of the tumor. The most common type of tumor that can be noted in adults are gliomas; these exhibit aggression and difficult prognosis [3,4]. Gliomas are brain tumors that cause a variety of adverse effects, including headaches, seizures, vision problems, and personality changes that can cause complete memory loss. In some severe cases, gliomas can cause death by putting pressure on the brain, which can damage brain tissue and impair brain function. Gliomas can also cause life-threatening complications like brain hemorrhage, brain herniation, and hydrocephalus; thus, detection at early stages can increase the chances of the patient's recovery after treatment. Over the last decade, we have witnessed substantial development in medical imaging technologies, and they are now becoming an integral part of diagnosis and treatment processes. Magnetic resonance imaging (MRI) is required to asses the heterogeneity of the tumor; these MRI scans include T1 (T1-weighted; this shows the structure and composition of the brain tissue), T1 ce (T1 enhanced with gadolinium agent; this improves the visibility of abnormalities or anomalies in the brain tissue [5]), T2 (T2-weighted; this highlights the fluid content in the brain tissue [6]), T2-FLAIR (fluid-attenuated inversion recovery; this suppresses the fluid signals in order to make it very easy to identify lesions that are very hard to spot in all the other channels [6]), as shown in Figure 1. These MRI scans help in the identification of four different regions of tumors in the brain, the first of which is a necrotic tumor (NCR), which specifies regions of dead or dying cells within the tumor mass because of insufficient blood supply; these tumors progress very rapidly, making them hard to identify and treat [6]. A non-enhancing tumor (NET) is a type of lesion that is less aggressive in nature when compared to enhancing tumors; these are basically slow-growing tumors. The treatment for these type of tumors is challenging because of their potential to progress within the tissue. Peritumoral edema (ED) refers to the swelling of the tumor due to excess fluid content within the brain tissue. This occurs because of the disrupted blood-brain barrier integrity, which leads to excess pressure within the skull [6]. This edema appears as an area of hyper-intensity, which differentiates it from the tumor core. A GD-enhancing tumor (ET) signifies an aggressive high-grade tumor along with active growth and increased vascularity. These regions help in assessing the tumor boundaries and monitoring treatment. The most challenging part of this problem is the identification of the type of tumor because of their complex structures and the variability of their appearances across different patients [7]. To address all these problems, it is essential to develop a system that can accurately segment and detect tumors in the brain. In recent years, foundation models for vision tasks have proven to be robust and effective in various domains of image segmentation, which has led to researchers exploring their usage in medical science [8]. Thus, in this paper, we propose a deep learning model, SAM2-Unet, which is a fusion of two different models, SAM2 and U-Net, that yields better accuracy and efficiency when analyzing a medical image [9].