Technol Health Care. 2022;30(S1):81-89. doi: 10.3233/THC-228008.
Classifying T1-weighted Magnetic Resonance brain scans into cerebrospinal fluid, gray matter and white matter is one of the most critical tasks in neurodegenerative disease analysis. Since manual delineation is a labor-intensive and time-consuming process, automated methods have been widely adopted for this purpose. One group of commonly used method by biomedical researchers are based on Gaussian mixture model. The main drawbacks of this model include complex computational cost and parameter selection with the presence of imaging defects such as intensity inhomogeneity and noise.
To alleviate these aspects, an improved Gaussian mixture model-based method is proposed in this work.
Standard mixture model was used to formulate individual voxel intensity. A set of spatial weightings were created to represent local tissue characteristics. The emphasis of this method is its "lite" and robust implementation mode highlighted by a dedicated entropy term. The Expectation-Maximization algorithm was then iteratively executed to estimate model parameters. The Maximum a Posteriori criterion was employed to determine for each voxel if it belongs to a certain tissue.
The proposed method was validated on both simulated and real MR scans. The averaged Dice coefficient of segmented brain tissues on each dataset ranged between [66.41, 87.42] for cerebrospinal fluid, [80.57, 85.35] for gray matter, and [83.17, 85.63] for white matter.
Experiments illustrated the effectiveness and reliability in tissue classification against imaging defects compared with manually constructed reference standard.
将 T1 加权磁共振脑扫描分类为脑脊液、灰质和白质是神经退行性疾病分析中最关键的任务之一。由于手动勾画是一项劳动密集型且耗时的过程，因此已广泛采用自动化方法来实现这一目标。生物医学研究人员常用的一类方法是基于高斯混合模型。该模型的主要缺点包括计算成本复杂，以及存在成像缺陷（如强度不均匀和噪声）时的参数选择问题。
为了缓解这些问题，本研究提出了一种改进的基于高斯混合模型的方法。
标准混合模型用于描述单个体素的强度。创建了一组空间权重来表示局部组织特征。该方法的重点是其“精简”且稳健的实现模式，突出了专门的熵项。然后，通过迭代执行期望最大化算法来估计模型参数。最大后验准则用于确定每个体素属于特定组织。
该方法在模拟和真实磁共振扫描上进行了验证。在每个数据集上，分割脑组织的平均 Dice 系数在脑脊液中为[66.41, 87.42]，在灰质中为[80.57, 85.35]，在白质中为[83.17, 85.63]。
与手动构建的参考标准相比，实验表明该方法在组织分类方面具有针对成像缺陷的有效性和可靠性。
