Self-Prompting Hybrid YOLOv12-SAM 2 Model for MRI Brain Tumour Segmentation in Real Time

Introduction The brain is the main organ of the nervous system and serves as the command and control centre for all bodily functions required to maintain a normal, healthy life. Brain tumours are characterised by the growth of abnormal cells in the brain, which disrupts healthy cerebral tissue. Early diagnosis and effective treatment require the timely identification and segmentation of brain tumours. Traditional methods of diagnosing brain tumours include manually reviewing MRIs, which is a laborious and error-prone procedure. Researchers have recently developed many novel automated methods for detecting and segmenting brain tumours in magnetic resonance imaging data. These useful techniques have brought tremendous improvement in the precision and speed of medical image analysis, eventually leading to more accurate diagnoses and optimised treatment plans. Materials and Methods In this study, a new method was introduced to use Segment Anything Model 2 (SAM 2) with the YOLOv12 model to detect and segment brain tumour using MRI. In this approach, the predictions of the YOLOv12 bounding box tumour were used to automatically generate input prompts for SAM 2, reducing the need for manual annotations. Then it was applied to a benchmark figshare image dataset where it performed better than the state-of-the-art in the tumor segmentation task. Results This approach achieved 99.71% segmentation accuracy and a Dice coefficient of 91.85%. Discussion Compared with state-of-the-art models, the proposed model outperforms in terms of segmentation accuracy and the Dice score. Conclusion This study indicates that the use of this hybrid model for radiological analysis will significantly increase the accuracy and speed of radiological analysis, with the potential to aid in clinical decision-making and patient care.