A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology
A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology
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DOI：
10.1109/tmi.2017.2677499
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发表时间：
2017-07-01
影响因子：
10.6
通讯作者：
Sethi, Amit
中科院分区：
文献类型：
--
作者：
Kumar, Neeraj;Verma, Ruchika;Sethi, Amit
数字显微组织图像中的细胞核分割可以提取高质量的特征，用于细胞核形态测量和计算病理学中的其他分析。传统的图像处理技术，如大津阈值和分水岭分割，不能有效地工作在具有挑战性的情况下，如染色质稀疏和拥挤的细胞核。相比之下，基于机器学习的分割可以概括各种核外观。然而，训练机器学习算法需要图像数据集，其中大量的细胞核已经被注释。公共访问和注释的数据集，沿着广泛同意的度量比较技术，已经催化了巨大的创新和进步的其他图像分类问题，特别是在对象识别。受他们成功的启发，我们引入了一个大型的公开访问的苏木精和伊红（H&E）染色的组织图像数据集，其中有超过21000个精心注释的核边界，其质量得到了医学医生的验证。由于我们的数据集来自多家医院，包括来自多个患者、疾病状态和器官的多种核外观，因此在其上训练的技术可能会很好地推广，并在其他H& E染色图像上开箱即用。我们还提出了一个新的指标来评估核分割结果，以统一的方式惩罚对象和像素级错误，而不是以前的指标只惩罚一种类型的错误。我们还提出了一种基于深度学习的分割技术，该技术特别强调识别核边界，包括接触或重叠的核之间的边界，并且在各种测试图像上都能很好地工作。
Nuclear segmentation in digital microscopic tissue images can enable extraction of high-quality features for nuclear morphometrics and other analysis in computational pathology. Conventional image processing techniques, such as Otsu thresholding and watershed segmentation, do not work effectively on challenging cases, such as chromatin-sparse and crowded nuclei. In contrast, machine learning-based segmentation can generalize across various nuclear appearances. However, training machine learning algorithms requires data sets of images, in which a vast number of nuclei have been annotated. Publicly accessible and annotated data sets, along with widely agreed upon metrics to compare techniques, have catalyzed tremendous innovation and progress on other image classification problems, particularly in object recognition. Inspired by their success, we introduce a large publicly accessible data set of hematoxylin and eosin (H&E)-stained tissue images with more than 21 000 painstakingly annotated nuclear boundaries, whose quality was validated by a medical doctor. Because our data set is taken from multiple hospitals and includes a diversity of nuclear appearances from several patients, disease states, and organs, techniques trained on it are likely to generalize well and work right out-of-the-box on other H&E-stained images. We also propose a new metric to evaluate nuclear segmentation results that penalizes object-and pixel-level errors in a unified manner, unlike previous metrics that penalize only one type of error. We also propose a segmentation technique based on deep learning that lays a special emphasis on identifying the nuclear boundaries, including those between the touching or overlapping nuclei, and works well on a diverse set of test images.