Intracranial hemorrhage occurs when blood vessels rupture or leak within the brain tissue or elsewhere inside the skull. It can be caused by physical trauma or by various medical conditions and in many cases leads to death. The treatment must be started as soon as possible, and therefore the hemorrhage should be diagnosed accurately and quickly. The diagnosis is usually performed by a radiologist who analyses a Computed Tomography (CT) scan containing a large number of cross-sectional images throughout the brain. Analysing each image manually can be very time-consuming, but automated techniques can help speed up the process. While much of the recent research has focused on solving this problem by using supervised machine learning algorithms, publicly-available training data remains scarce due to privacy concerns. This problem can be alleviated by unsupervised algorithms. In this paper, we propose a fully-unsupervised algorithm which is based on the mixture models. Our algorithm utilizes the fact that the properties of hemorrhage and healthy tissues follow different distributions, and therefore an appropriate formulation of these distributions allows us to separate them through an Expectation-Maximization process. In addition, our algorithm is able to adaptively determine the number of clusters such that all the hemorrhage regions can be found without including noisy voxels. We demonstrate the results of our algorithm on publicly-available datasets that contain all different hemorrhage types in various sizes and intensities, and our results are compared to earlier unsupervised and supervised algorithms. The results show that our algorithm can outperform the other algorithms with most hemorrhage types.
翻译:当血管在大脑组织内部或头骨内其他地方破裂或泄漏时,就会出现内出血。 它可以是身体创伤或各种医疗条件造成的,而且在许多情况下会导致死亡。 治疗必须尽快开始, 因此出血应准确和快速诊断。 诊断通常由一名放射学家进行, 他分析一个包含大量整个大脑跨部门图像的剖析(CT)扫描。 人工分析每个图像的特性可能非常耗时, 但自动化技术可以帮助加快过程。 虽然最近许多研究的重点是通过使用受监督的机器学习算法来解决这一问题, 但由于隐私问题, 公开获得的培训数据仍然很少。 这个问题可以通过不受监督的算法来缓解。 在本文中, 我们提出一个完全不受监督的算法, 它以混合物模型为基础。 我们的算法利用了一个事实, 我们的出血和健康组织的性质可以遵循不同的分布, 并且这些分布的恰当配方可以让我们通过一个早期的机器学习算法 来区分它们, 并且通过一个没有受到监督的算算法, 包括不具有适应性算法的变异的算法, 我们所有的变算法, 能够显示我们所有的变形的变形的变形的算法 。