Cavum Septum Pellucidum (CSP) is a critical brain structure frequently evaluated in neurological and psychiatric assessments which requires precise identification and segmentation for accurate diagnosis and evaluation. This paper introduces the YOLO (You Only Look Once) model within an improved framework of advanced methods for data augmentation. It incorporates an optimized YOLOv9c model which is specifically designed for high-resolution medical imaging that helps to identify key points and outline the boundaries of the CSP. This research paper use four different YOLO version algorithms: YOLOv8m, YOLOv8n, YOLOv8x, and YOLOv9c. Among the models, the YOLOv9c demonstrates significant enhancements in segmentation accuracy attaining high precision (0.962), recall (0.946), and mean average precision (0.980). We evaluate the performance of our model using a comprehensive dataset of brain MRI (Magnetic Resonance Imaging) and it shows robustness and adaptability across a wide range of imaging conditions. In this study, observations indicate that integrating point detection into instance segmentation tasks enhances localization accuracy and optimizes the segmentation process, which makes it highly suitable for real-time clinical applications. This research highlights the promise of integrating state-of-the-art CNN (Convolutional Neural Network) architectures with innovative detection techniques to advance the analysis of medical images, especially within neuroimaging.