论文标题
一种基于共识的生物医学图像分割的动力学方法
A kinetic approach to consensus-based segmentation of biomedical images
论文作者
论文摘要
在这项工作中，我们将动力学版本应用于生物医学分割问题。在提出的方法中，有关每个粒子/像素的微观状态的时间相关信息包括其空间位置和代表系统静态特征的特征，即每个像素的灰度。从引入的微观模型中，我们得出了模型的动力学公式。然后，借助替代fokker-planck方法来计算系统的巨大时间行为，该方法可以在准不变缩放中获得。我们利用直接仿真蒙特卡洛方法的计算效率来用于获得参数识别任务的问题的玻尔兹曼类型描述。基于测量地面真相分割面罩和评估掩模之间距离的合适损耗函数，我们将引入的分割度量度量最小化，以示为一组相关的2D灰度图像。生物医学分割的应用集中在不同的成像研究环境上。
In this work, we apply a kinetic version of a bounded confidence consensus model to biomedical segmentation problems. In the presented approach, time-dependent information on the microscopic state of each particle/pixel includes its space position and a feature representing a static characteristic of the system, i.e. the gray level of each pixel. From the introduced microscopic model we derive a kinetic formulation of the model. The large time behavior of the system is then computed with the aid of a surrogate Fokker-Planck approach that can be obtained in the quasi-invariant scaling. We exploit the computational efficiency of direct simulation Monte Carlo methods for the obtained Boltzmann-type description of the problem for parameter identification tasks. Based on a suitable loss function measuring the distance between the ground truth segmentation mask and the evaluated mask, we minimize the introduced segmentation metric for a relevant set of 2D gray-scale images. Applications to biomedical segmentation concentrate on different imaging research contexts.
