Published Paper Details:

Brain tumour segmentation is a widely explored area of research within both medical and engineering domains, particularly focusing on Magnetic Resonance Imaging (MRI) as the primary modality for detection. Computer-aided diagnosis (CADx) systems play a pivotal role in automating the detection and classification of brain tumours from MRI images. A crucial component of CADx systems is the segmentation module, responsible for precisely delineating tumour regions. Accurate segmentation, with sub-pixel precision, is essential for determining tumor size, and location, and facilitating image-guided surgical procedures. In recent years, numerous methods have emerged for segmenting brain tumours from MRI images. This paper presents a comprehensive survey of state-of-the-art segmentation methods, along with an overview of datasets commonly used for method development and the evaluation metrics employed. By offering a consolidated overview, this study serves as a valuable resource for novice researchers entering this field and provides updated information for researchers already engaged in brain tumour segmentation research.