Brain structure segmentation, including tumors, in medical imaging has become a necessity to help neurologists correctly diagnose patients' conditions. The complexity of these structures requires the implementation of automatic segmentation methods, often developed by magnetic resonance imaging. This study aims to design and implement an automatic system for detecting and localizing tumor regions by combining three different methods. Firstly, the region of interest, that is, the pixels belonging to the tumor, is detected using Random Forest's algorithm, while the rest of the pixels of the image are considered to belong to the background. Thus, the tumor and background seeds are obtained, automatically, for segmentation using the Graph Cut method. This segmentation allows to obtain the initial contour, for the level set (LVS) segmentation, which refine the previous segmentation. The proposed method was validated on the Multimodal Brain Tumor Segmentation Challenge (BRATS) database (http://braintumorsegmentation.org; 2015).

中文翻译：

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用于计算机辅助诊断系统的自动脑肿瘤分割

医学成像中包括肿瘤在内的脑结构分割已成为帮助神经科医生正确诊断患者病情的必要条件。这些结构的复杂性需要实施自动分割方法，通常由磁共振成像开发。本研究旨在通过结合三种不同的方法来设计和实现用于检测和定位肿瘤区域的自动系统。首先，使用随机森林算法检测感兴趣区域，即属于肿瘤的像素，而图像的其余像素被认为属于背景。因此，肿瘤和背景种子是自动获得的，用于使用 Graph Cut 方法进行分割。这种分割允许获得初始轮廓，用于水平集（LVS）分割，这改进了之前的分割。所提出的方法在多模式脑肿瘤分割挑战 (BRATS) 数据库 (http://braintumorsegmentation.org; 2015) 上得到验证。