Context: Image segmentation pipelines often are sensitive to algorithm input parameters. Algorithm parameters optimized for a set of images do not necessarily produce good-quality-segmentation results for other images. Even within an image, some regions may not be well segmented due to a number of factors, including multiple pieces of tissue with distinct characteristics, differences in staining of the tissue, normal versus tumor regions, and tumor heterogeneity. Evaluation of quality of segmentation results is an important step in image analysis. It is very labor intensive to do quality assessment manually with large image datasets because a whole-slide tissue image may have hundreds of thousands of nuclei. Semi-automatic mechanisms are needed to assist researchers and application developers to detect image regions with bad segmentations efficiently. Aims: Our goal is to develop and evaluate a machine-learning-based semi-automated workflow to assess quality of nucleus segmentation results in a large set of whole-slide tissue images. Methods: We propose a quality control methodology, in which machine-learning algorithms are trained with image intensity and texture features to produce a classification model. This model is applied to image patches in a whole-slide tissue image to predict the quality of nucleus segmentation in each patch. The training step of our methodology involves the selection and labeling of regions by a pathologist in a set of images to create the training dataset. The image regions are partitioned into patches. A set of intensity and texture features is computed for each patch. A classifier is trained with the features and the labels assigned by the pathologist. At the end of this process, a classification model is generated. The classification step applies the classification model to unlabeled test images. Each test image is partitioned into patches. The classification model is applied to each patch to predict the patch's label. Results: The proposed methodology has been evaluated by assessing the segmentation quality of a segmentation method applied to images from two cancer types in The Cancer Genome Atlas; WHO Grade II lower grade glioma (LGG) and lung adenocarcinoma (LUAD). The results show that our method performs well in predicting patches with good-quality segmentations and achieves F1 scores 84.7% for LGG and 75.43% for LUAD. Conclusions: As image scanning technologies advance, large volumes of whole-slide tissue images will be available for research and clinical use. Efficient approaches for the assessment of quality and robustness of output from computerized image analysis workflows will become increasingly critical to extracting useful quantitative information from tissue images. Our work demonstrates the feasibility of machine-learning-based semi-automated techniques to assist researchers and algorithm developers in this process.
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机译：背景：图像分割管道通常对算法输入参数敏感。针对一组图像优化的算法参数不一定会为其他图像产生高质量的分割结果。即使在图像内，由于许多因素，某些区域也可能无法很好地分割，包括具有不同特征的多块组织，组织染色的差异，正常区域与肿瘤区域以及肿瘤异质性。分割结果质量的评估是图像分析的重要步骤。手动使用大型图像数据集进行质量评估非常费力，因为整个幻灯片组织图像可能具有成千上万个核。需要半自动机制来协助研究人员和应用程序开发人员有效地检测出具有不良分割的图像区域。目的：我们的目标是开发和评估基于机器学习的半自动化工作流程，以评估大量全幻灯片组织图像中核分割结果的质量。方法：我们提出了一种质量控制方法，其中使用图像强度和纹理特征训练机器学习算法，以产生分类模型。该模型适用于整个幻灯片组织图像中的图像斑块，以预测每个斑块中的核分割质量。我们方法的训练步骤包括由病理学家在一组图像中选择和标记区域以创建训练数据集。图像区域被分成补丁。为每个补丁计算一组强度和纹理特征。使用病理学家分配的功能和标签来训练分类器。在此过程结束时，将生成一个分类模型。分类步骤将分类模型应用于未标记的测试图像。每个测试映像都划分为补丁。将分类模型应用于每个补丁，以预测补丁的标签。结果：通过评估应用于《癌症基因组图谱》中两种癌症类型的图像的分割方法的分割质量，对所提出的方法进行了评估。 WHO II级低级神经胶质瘤（LGG）和肺腺癌（LUAD）。结果表明，我们的方法在以高质量分段进行的补丁预测中表现良好，LGG的F1得分为84.7％，LUAD的F1得分为75.43％。结论：随着图像扫描技术的进步，大量的全幻灯片组织图像将可用于研究和临床使用。评估计算机图像分析工作流输出的质量和鲁棒性的有效方法对于从组织图像中提取有用的定量信息将变得越来越重要。我们的工作证明了基于机器学习的半自动化技术在此过程中协助研究人员和算法开发人员的可行性。
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