# PPA-SAM: Plug-and-Play Adversarial Segment Anything Model for 3D Tooth Segmentation
## 1 Introduction  
In the field of oral medicine, precise tooth segmentation is crucial for diagnosing oral diseases, formulating effective treatment plans, and restoring patients' oral structures [1; 2]. Medical imaging techniques such as X-rays, Cone Beam Computed Tomography (CBCT), Magnetic Resonance Imaging (MRI), etc., provide rich information about oral structures. By accurately segmenting tooth models from CBCT images, oral healthcare professionals can achieve more precise diagnoses of oral diseases, formulate personalized treatment plans effectively, and identify surface lesions and damages on teeth. This enables doctors to pinpoint and address specific areas with greater accuracy. For the restoration and reconstruction of oral structures, precise tooth segmentation provides crucial guidance for planning procedures such as dental implant placement and other deno-alveolar surgeries that involve cutting bone with minimal damage to roots. The bone cutting could be with burs or piezotome to remove impacted teeth or when performing corticotomies or osteotomies to remove bone blocks for grafting or accelerate orthodontic treatment, ensuring coordination with surrounding oral tissues. In addition, in orthognathic surgery, segmentation is equally crucial to prevent damage to tooth roots during osteotomy. Forinstance, in cases where the widening of the maxilla or the pushback of the premaxilla is planned, or when vertical movement of a section of the upper or lower jaw is required to correct an open bite. Artificial Intelligence has the potential to detect the severity of malocclusion and score it using occlusal indices or IOFTN [3]. Subsequently, this information can be utilized during orthognathic surgery to generate accurate segmentation and fabricate custom wafers [4]. Therefore, precise tooth segmentation not only enhances the accuracy of diagnostics in oral medicine but also promotes the precision of treatments, contributing to the effective restoration of oral structures. Before the advent of machine learning, researchers predominantly relied on the level-set method for the segmentation of teeth CBCT images [5; 6]. However, the level-set method typically requires an initial contour, which can impact the final segmentation results. Selecting a suitable initial contour may demand expertise or other prior information. Due to the potential existence of complex structures such as root canals and periodontal ligaments in CBCT images of teeth, the presence of these structures may make it challenging for the level set method to accurately segment the contours of each structure. With the advent of machine learning and neural networks, researchers have proposed various novel medical image segmentation networks, significantly reducing the time required for manual annotation by radiologists [7]. These networks not only diminish the subjective errors introduced by manual annotation but also enhance the stability and accuracy of segmentation results. Consequently, some researchers have applied deep learning methods to 3D tooth segmentation tasks [8; 9; 10; 11; 12]. This implies that doctors can rely more on these automated tools to quickly obtain precise segmentation results, providing more reliable support for patient diagnosis and treatment. Traditional convolutional networks often require extensive datasets to support them and are primarily designed for specific segmentation tasks, with weaker generalization performance. Recently, the Segment Anything Model (SAM) [13] has emerged, showcasing outstanding zero-shot segmentation performance in various everyday image segmentation tasks. This model, proposed by Meta AI, has pushed the boundaries of segmentation and significantly advanced the development of foundational models in computer vision. However, due to SAM's relatively limited training samples on medical image data, it exhibits a certain gap when applied to medical image segmentation tasks compared to existing models with precise segmentation and excellent performance.