Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features
Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features
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DOI：
10.1117/1.jmi.1.3.034003
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发表时间：
2014-10-01
影响因子：
2.4
通讯作者：
Madabhushi, Anant
中科院分区：
文献类型：
--
作者：
Wang, Haibo;Cruz-Roa, Angel;Madabhushi, Anant
乳腺癌（BCa）分级在预测疾病侵袭性和患者预后方面起着重要作用。BCa等级的关键组成部分是有丝分裂计数，其涉及量化分裂过程中的细胞数量（即，在特定的时间点进行有丝分裂）。目前，有丝分裂计数是由病理学家在显微镜下观察载玻片上的多个高倍视野（HPF）手动完成的，这是一个非常费力和耗时的过程。有丝分裂细胞核自动检测的计算机化系统的发展，虽然非常理想，但被有丝分裂的高度可变的形状和外观所混淆。现有的方法使用手工制作的特征，这些特征捕获有丝分裂的某些形态、统计或纹理属性，或者使用卷积神经网络（CNN）学习的特征。虽然手工制作的特征受到领域和特定应用的启发，但数据驱动的CNN模型往往是领域不可知的，并试图学习无法通过任何手工制作的特征表示的额外特征库。另一方面，CNN在计算上更复杂，需要大量标记的训练实例。由于手工特征试图对领域相关属性进行建模，而CNN方法在很大程度上是监督特征生成方法，因此尝试将这两种不同类别的特征生成策略联合收割机组合起来以创建一组集成的属性是有吸引力的，这些属性可能单独优于任何一类特征提取策略。我们提出了一种用于有丝分裂检测的级联方法，该方法智能地结合了CNN模型和手工特征（形态，颜色和纹理特征）。通过采用轻CNN模型，所提出的方法在计算上的要求要低得多，并且结合手工特征和CNN衍生特征的级联策略使得能够通过利用断开的特征集来最大化性能。对公共ICPR12有丝分裂数据集（由几位病理学家在35个HPF（放大400倍）上注释了226个有丝分裂）和15个测试HPF进行评价，得出F测量值为0.7345。我们的方法准确，快速，与现有方法相比需要更少的计算资源，使其在临床上可行。
Breast cancer (BCa) grading plays an important role in predicting disease aggressiveness and patient outcome. A key component of BCa grade is the mitotic count, which involves quantifying the number of cells in the process of dividing (i.e., undergoing mitosis) at a specific point in time. Currently, mitosis counting is done manually by a pathologist looking at multiple high power fields (HPFs) on a glass slide under a microscope, an extremely laborious and time consuming process. The development of computerized systems for automated detection of mitotic nuclei, while highly desirable, is confounded by the highly variable shape and appearance of mitoses. Existing methods use either handcrafted features that capture certain morphological, statistical, or textural attributes of mitoses or features learned with convolutional neural networks (CNN). Although handcrafted features are inspired by the domain and the particular application, the data-driven CNN models tend to be domain agnostic and attempt to learn additional feature bases that cannot be represented through any of the handcrafted features. On the other hand, CNN is computationally more complex and needs a large number of labeled training instances. Since handcrafted features attempt to model domain pertinent attributes and CNN approaches are largely supervised feature generation methods, there is an appeal in attempting to combine these two distinct classes of feature generation strategies to create an integrated set of attributes that can potentially outperform either class of feature extraction strategies individually. We present a cascaded approach for mitosis detection that intelligently combines a CNN model and handcrafted features (morphology, color, and texture features). By employing a light CNN model, the proposed approach is far less demanding computationally, and the cascaded strategy of combining handcrafted features and CNN-derived features enables the possibility of maximizing the performance by leveraging the disconnected feature sets. Evaluation on the public ICPR12 mitosis dataset that has 226 mitoses annotated on 35 HPFs (400x magnification) by several pathologists and 15 testing HPFs yielded an F-measure of 0.7345. Our approach is accurate, fast, and requires fewer computing resources compared to existent methods, making this feasible for clinical use.