论文标题
磁共振图像中脑肿瘤的感兴趣区域鉴定
Region of Interest Identification for Brain Tumors in Magnetic Resonance Images
论文作者
论文摘要
神经胶质瘤是一种常见的脑肿瘤类型，准确检测在诊断和治疗过程中起着至关重要的作用。尽管医学图像分析的进展，但由于肿瘤质地，位置和形状的变化，大脑磁共振（MR）图像中的准确肿瘤分割仍然是一个挑战。在本文中，我们提出了一种具有光计算复杂性的快速自动化方法，以找到肿瘤区域周围最小的边界框。该区域可用作用于亚区域肿瘤分割的训练网络的预处理步骤。通过采用该算法的输出，删除了冗余信息。因此，网络可以专注于学习与子区域类有关的著名功能。所提出的方法具有六个主要阶段，其中大脑分割是最重要的一步。预期最大化（EM）和K-均值算法用于脑部分割。在BRATS 2015数据集上评估了所提出的方法，并且获得的平均骰子得分为0.73，这是本应用程序的可接受结果。
Glioma is a common type of brain tumor, and accurate detection of it plays a vital role in the diagnosis and treatment process. Despite advances in medical image analyzing, accurate tumor segmentation in brain magnetic resonance (MR) images remains a challenge due to variations in tumor texture, position, and shape. In this paper, we propose a fast, automated method, with light computational complexity, to find the smallest bounding box around the tumor region. This region-of-interest can be used as a preprocessing step in training networks for subregion tumor segmentation. By adopting the outputs of this algorithm, redundant information is removed; hence the network can focus on learning notable features related to subregions' classes. The proposed method has six main stages, in which the brain segmentation is the most vital step. Expectation-maximization (EM) and K-means algorithms are used for brain segmentation. The proposed method is evaluated on the BraTS 2015 dataset, and the average gained DICE score is 0.73, which is an acceptable result for this application.
