Since nuclei segmentation in histopathology images can provide key information for identifying the presence or stage of a disease, the images need to be assessed carefully. However, color variation in histopathology images, and various structures of nuclei are two major obstacles in accurately segmenting and analyzing histopathology images. Several machine learning methods heavily rely on hand-crafted features which have limitations due to manual thresholding. To obtain robust results, deep learning based methods have been proposed. Deep convolutional neural networks (DCNN) used for automatically extracting features from raw image data have been proven to achieve great performance. Inspired by such achievements, we propose a nuclei segmentation method based on DCNNs. To normalize the color of histopathology images, we use a deep convolutional Gaussian mixture color normalization model which is able to cluster pixels while considering the structures of nuclei. To segment nuclei, we use Mask R-CNN which achieves state-of-the-art object segmentation performance in the field of computer vision. In addition, we perform multiple inference as a post-processing step to boost segmentation performance. We evaluate our segmentation method on two different datasets. The first dataset consists of histopathology images of various organ while the other consists histopathology images of the same organ. Performance of our segmentation method is measured in various experimental setups at the object-level and the pixel-level. In addition, we compare the performance of our method with that of existing state-of-the-art methods. The experimental results show that our nuclei segmentation method outperforms the existing methods. We propose a nuclei segmentation method based on DCNNs for histopathology images. The proposed method which uses Mask R-CNN with color normalization and multiple inference post-processing provides robust nuclei segmentation results. Our method also can facilitate downstream nuclei morphological analyses as it provides high-quality features extracted from histopathology images.

中文翻译：

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一种基于深度卷积神经网络的组织病理学图像自动细胞核分割方法。


由于组织病理学图像中的细胞核分割可以提供识别疾病的存在或阶段的关键信息，因此需要仔细评估图像。然而，组织病理学图像的颜色变化和细胞核的不同结构是准确分割和分析组织病理学图像的两个主要障碍。几种机器学习方法严重依赖于手工制作的特征，这些特征由于手动阈值设置而受到限制。为了获得稳健的结果，人们提出了基于深度学习的方法。用于从原始图像数据中自动提取特征的深度卷积神经网络（DCNN）已被证明可以实现出色的性能。受这些成果的启发，我们提出了一种基于 DCNN 的细胞核分割方法。为了标准化组织病理学图像的颜色，我们使用深度卷积高斯混合颜色标准化模型，该模型能够在考虑细胞核结构的同时对像素进行聚类。为了分割细胞核，我们使用 Mask R-CNN，它在计算机视觉领域实现了最先进的对象分割性能。此外，我们还执行多重推理作为后处理步骤，以提高分割性能。我们在两个不同的数据集上评估我们的分割方法。第一个数据集由各种器官的组织病理学图像组成，而另一个数据集由同一器官的组织病理学图像组成。我们的分割方法的性能是在对象级和像素级的各种实验设置中测量的。此外，我们将我们的方法的性能与现有最先进方法的性能进行了比较。实验结果表明，我们的核分割方法优于现有方法。 我们提出了一种基于 DCNN 的组织病理学图像的细胞核分割方法。所提出的方法使用带有颜色归一化和多重推理后处理的 Mask R-CNN，提供了稳健的细胞核分割结果。我们的方法还可以促进下游细胞核形态分析，因为它提供了从组织病理学图像中提取的高质量特征。