HEp-2 Specimen Cell Detection and Classification Using Very Deep Convolutional Neural Networks-Based Cell Shape

Brandon Jorgensen, Khamael Al-Dulaimi, Jasmine Banks
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引用次数: 3

Abstract

The accurate detection and classification of HEp-2 specimen staining plays a key role in autoimmune disease diagnosis and transplantation assessment. Such detection and classification is challenging due to the abundant presence of highly overlapped cells, variations in cell densities, the variety of staining patterns, large numbers of cells per image, large data volumes and overfitting of features. In this paper, a robust technique is proposed to deal with images of all staining patterns and address these challenges. Very deep convolutional neural networks with a layer structure inspired by the standard architecture of the VGG-16 image is proposed for classification of HEp-2 staining cells based on cell shape and adapted to consider overfitting. Level set method using geometric active contours with morphological opening and Delaunay triangulation is used for cell segmentation and splitting. The cell segmentation method also considers overlapped cells. The proposed method has been tested and compared with other methods using Task-2 training dataset from competitions held on the ICPR2014 and ICPR2016. A extensive study demonstrates that the proposed method outperforms all other methods and promises to support the diagnosis of autoimmune diseases and allograft rejection prediction in future pathology practice, except for one method in this study which is slightly better than our proposed method.
基于深度卷积神经网络的HEp-2细胞形态检测与分类
HEp-2标本染色的准确检测和分类在自身免疫性疾病诊断和移植评估中具有关键作用。由于存在大量高度重叠的细胞、细胞密度的变化、染色模式的变化、每张图像的大量细胞、大数据量和特征的过拟合,这种检测和分类具有挑战性。本文提出了一种鲁棒的技术来处理所有染色模式的图像并解决这些挑战。基于VGG-16图像的标准结构,提出了一种层状结构的深度卷积神经网络,用于基于细胞形状对HEp-2染色细胞进行分类,并适应过拟合。采用带形态开口的几何活动轮廓和Delaunay三角剖分的水平集方法进行细胞分割和分裂。细胞分割方法还考虑了重叠的细胞。使用ICPR2014和ICPR2016比赛的Task-2训练数据集对所提出的方法进行了测试并与其他方法进行了比较。一项广泛的研究表明,该方法优于所有其他方法,并有望在未来的病理实践中支持自身免疫性疾病的诊断和异体移植排斥反应的预测,除了本研究中的一种方法略优于我们提出的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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