The latest revision of multiple sclerosis diagnosis guidelines emphasizes the role of oligoclonal band detection in isoelectric focusing images of cerebrospinal fluid. Recent studies suggest tears as a promising noninvasive alternative to cerebrospinal fluid. We are developing the first automatic method for isoelectric focusing image analysis and oligoclonal band detection in cerebrospinal fluid and tear samples. The automatic analysis would provide an accurate, fast analysis and would reduce the expert-dependent variability and errors of the current visual analysis. In this paper, we describe a new effective model for the fully automated segmentation of highly distorted lanes in isoelectric focusing images. This approach is a new formulation of the classic parametric active contour problem, in which an open active contour is constrained to move from the top to the bottom of the image, and the x-axis coordinate is expressed as a function of the y-axis coordinate. The left and right edges of the lane evolved together in a ribbon-like shape so that the full width of the lane was captured reliably. The segmentation algorithm was implemented using a multiresolution approach in which the scale factor and the active contour control points were progressively increased. The lane segmentation algorithm was tested on a database of 51 isoelectric focusing images containing 419 analyzable lanes. The new model gave robust results for highly curved lanes, weak edges, and low-contrast lanes. A total of 98.8% of the lanes were perfectly segmented, and the remaining 1.2% had only minor errors. The computation time (1 s per membrane) is negligible. This method precisely defines the region of interest in each lane and thus is a major step toward the first fully automatic tool for oligoclonal band detection in isoelectric focusing images. Graphical abstract.

中文翻译：

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迈向用于多发性硬化症诊断的脑脊髓液和泪液中寡克隆带检测的自动工具：基于带状单变量开放活动轮廓的车道分割。

多发性硬化症诊断指南的最新修订强调了寡克隆带检测在脑脊髓液等电聚焦图像中的作用。最近的研究表明，眼泪是一种有希望的无创替代脑脊液的药物。我们正在开发首个自动等电聚焦图像分析和脑脊髓液和眼泪样品寡克隆带检测方法。自动分析将提供准确，快速的分析，并减少当前视觉分析的专家依赖的可变性和错误。在本文中，我们描述了一种用于等电聚焦图像中高度失真车道的全自动分割的新有效模型。这种方法是经典参数有效轮廓问题的新提法，其中打开的活动轮廓被约束为从图像的顶部移动到底部，并且x轴坐标表示为y轴坐标的函数。车道的左边缘和右边缘一起发展成带状形状，从而可靠地捕获了车道的整个宽度。使用多分辨率方法实现了分割算法，其中比例因子和有效轮廓控制点逐渐增加。在包含419条可分析车道的51个等电聚焦图像的数据库上测试了车道分割算法。新模型为高度弯曲的车道，弱边缘和低对比度的车道提供了可靠的结果。共有98.8％的车道被完美分割，其余1.2％的车道只有很小的错误。计算时间（每个膜1 s）可以忽略不计。该方法精确地定义了每个泳道中的目标区域，因此是向等电聚焦图像中的寡克隆带检测的第一个全自动工具迈出的重要一步。图形概要。