In this paper, we propose and analyze a novel framework to identify the HEp-2 specimen images, consisting of mitotic spindle (MS) pattern cells. It is based on the fact that the cells showing MS patterns will always be present with other interphase type pattern cells, in a whole slide image (WSI) or specimen image, but the number of MS cells will be very small, unlike interphase patterns. Considering this fact, the work contributes in presenting a framework, using different strategies such as cells-based, region-based and complete image-based approaches.

For cell-based approach, the distinctive characteristic of MS patterns is represented using morphology and texture-based features, followed by a traditional Support Vector Machine (SVM) based classifier, whereas the region-based approach uses a Convolutional Neural Network (CNN) as feature extractor and baseline classifier. Finally, for the image-based approach, the Faster Region-CNN (Faster-RCNN) based object detection framework has been applied, considering MS patterns as distinct objects. The region and image-based approaches also contribute in avoiding the requirement of DAPI based segmentation masks. Another contribution of this work is to use a novel and clearly specified threshold-based decision-making criteria for patterns declaration of the specimens with MS pattern cells.

All the proposed strategies, integrated with decision-making criteria are validated on a publicly available dataset and across various experiments, we demonstrate good performance, i.e., max. MCC 0.92 in one case. Hence, the proposed framework proves to be an effective solution for the problem statement.

在本文中，我们提出并分析了一种新颖的框架来鉴定由有丝分裂纺锤体（MS）模式细胞组成的HEp-2标本图像。基于这样的事实：在整个幻灯片图像（WSI）或样本图像中，显示MS模式的单元将始终与其他相间类型的模式单元一起出现，但是与相间模式不同，MS单元的数量将非常少。考虑到这一事实，这项工作有助于提出一个框架，使用不同的策略，例如基于单元格，基于区域和基于完整图像的方法。

对于基于细胞的方法，使用形态学和基于纹理的特征来表示MS模式的独特特征，然后使用基于传统支持向量机（SVM）的分类器，而基于区域的方法则使用卷积神经网络（CNN）作为特征提取器和基线分类器。最后，对于基于图像的方法，考虑到MS模式作为不同的对象，已应用了基于Faster Region-CNN（Faster-RCNN）的对象检测框架。基于区域和基于图像的方法还有助于避免基于DAPI的分割掩码的需求。这项工作的另一个贡献是使用一种新颖且明确指定的基于阈值的决策标准来对具有MS模式单元的标本进行模式声明。

所有建议的策略，与决策标准相结合，均在公开数据集上进行了验证，并且在各种实验中均得到了良好的表现，即最大 MCC 0.92（一种情况）。因此，所提出的框架被证明是问题陈述的有效解决方案。