Classification of mitotic figures with convolutional neural networks and seeded blob features.
Classification of mitotic figures with convolutional neural networks and seeded blob features.
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DOI：
10.4103/2153-3539.112694
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发表时间：
2013
影响因子：
--
通讯作者：
Cosatto E
中科院分区：
文献类型：
--
作者：
Malon CD;Cosatto E
2012年国际模式识别会议(ICPR)上的有丝分裂图形识别大赛挑战了一种系统，该系统使用三台扫描仪(Aperio、Hamamatsu和多光谱)中的每一台来识别苏木素和曙红染色组织中的所有有丝分裂图形。我们的方法结合了人工设计的核特征和卷积神经网络(CNN)提取的学习特征。核特征捕捉核周围分割区域的颜色、纹理和形状信息。CNN的使用处理了有丝分裂图形的各种外观，并降低了对手动制作的特征和阈值的敏感性。在大赛提供的测试集上，经过训练的系统在彩色扫描仪上获得了高达0.659分的F1分数，在多光谱扫描仪上获得了0.589分。我们展示了一种将基于分割的特征与CNN相结合的强大技术，以相当的精度识别大多数有丝分裂图形。此外，我们还表明，该方法无需进行重大重新设计，即可容纳来自多光谱扫描仪的额外焦平面和光谱带的信息。
The mitotic figure recognition contest at the 2012 International Conference on Pattern Recognition (ICPR) challenges a system to identify all mitotic figures in a region of interest of hematoxylin and eosin stained tissue, using each of three scanners (Aperio, Hamamatsu, and multispectral). Our approach combines manually designed nuclear features with the learned features extracted by convolutional neural networks (CNN). The nuclear features capture color, texture, and shape information of segmented regions around a nucleus. The use of a CNN handles the variety of appearances of mitotic figures and decreases sensitivity to the manually crafted features and thresholds. On the test set provided by the contest, the trained system achieves F1 scores up to 0.659 on color scanners and 0.589 on multispectral scanner. We demonstrate a powerful technique combining segmentation-based features with CNN, identifying the majority of mitotic figures with a fair precision. Further, we show that the approach accommodates information from the additional focal planes and spectral bands from a multi-spectral scanner without major redesign.